Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in ... more Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in Underwater Wireless Sensor Networks (UWSNs). In this paper, we present a Balanced Load Distribution (BLOAD) scheme to avoid energy holes created due to unbalanced energy consumption in UWSNs. Our proposed scheme prolongs the stability period and lifetime of the UWSNs. In BLOAD scheme, data (generated plus received) of underwater sensor nodes is divided into fractions. The transmission range of each sensor node is logically adjusted for evenly distributing the data fractions among the next hop neighbor nodes. Another distinct feature of BLOAD scheme is that each sensor node in the network sends a fraction of data directly to the sink by adjusting its transmission range and continuously reports data to the sink till its death even if an energy hole is created in its next hop region. We implement the BLOAD scheme, by varying the fractions of data using adjustable transmission ranges in homogeneous and heterogeneous simulation environments. Simulation results show that the BLOAD scheme outperforms the selected existing schemes in terms of stability period and network lifetime. INDEX TERMS Underwater wireless sensor networks, energy hole, balance load distribution, weight, energy balancing, stability.
Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefor... more Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefore, it is important to know the accurate position of the sensor node in large scale applications like disaster prevention, tactical surveillance, and monitoring. Due to the inefficiency of the global positioning system (GPS) in UWSN, it is very difficult to localize a node in underwater environment compared to terrestrial networks. To minimize the localization error and enhance the localization coverage of the network, two routing protocols are proposed; the first one is mobile autonomous underwater vehicle (MobiL-AUV) and the second one is cooperative MobiL (CO-MobiL). In MobiL-AUV, AUVs are deployed and equipped with GPS and act as reference nodes. These reference nodes are used to localize all the nonlocalized ordinary sensor nodes in order to reduce the localization error and maximize the network coverage. CO-MobiL is presented in order to improve the network throughput by using the maximal ratio combining (MRC) as diversity technique which combines both signals, received from the source and received from the relay at the destination. It uses amplify-and-forward (AF) mechanism to improve the signal between the source and the destination. To support our claims, extensive simulations are performed.
Cooperative routing is an appealing challenge in underwater wireless sensor networks (UWSNs). In ... more Cooperative routing is an appealing challenge in underwater wireless sensor networks (UWSNs). In this paper, we propose a region based cooperative routing protocol (RBCRP) for amplify and forward (AF) technique over Rayleigh faded channels in UWSNs. The source node sends the sensed signal to the destination and available relay nodes. At the destination node, bit error rate (BER) is checked on the basis of which, either positive or negative acknowledgement (ACK or NACK) is sent to the source and relay nodes. We also use mobile sinks (MSs) and energy harvesting techniques to further prolong the network lifetime and maximize the throughput. Our derived mathematical equations for the SNR gain and outage probability are verified by simulations. Results show that RBCRP performs better than the existing incremental best relay technique (IBRT) in terms of throughput, network lifetime and outage probability.
—Short-term load forecasting (STLF) models are very important for electric industry in the trade ... more —Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.
One of the major challenges in design of wireless sensor networks (WSNs) is to reduce energy cons... more One of the major challenges in design of wireless sensor networks (WSNs) is to reduce energy consumption of sensor nodes to prolong lifetime of finite capacity batteries. In this paper, we propose energy-efficient adaptive scheme for transmission (EAST) in WSNs. EAST is an IEEE 802.15.4 standard compliant. In this scheme, open-looping feedback process is used for temperature-aware link quality estimation and compensation, wherea closed-loop feedback process helps to divide network into three logical regions to minimize overhead of control packets. Threshold on transmitter power loss (RSSI loss) and current number of nodes (í µí± í µí± (í µí±¡)) in each region help to adapt transmit power level (í µí± level) according to link quality changes due to temperature variation. Evaluation of the proposed scheme is done by considering mobile sensor nodes and reference node both static and mobile. Simulation results show that the proposed scheme effectively adapts transmission í µí± level to changing link quality with less control packets overhead and energy consumption as compared to classical approach with single region in which maximum transmitter í µí± level assigned to compensate temperature variation.
Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one... more Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one of the key strategies in reducing packet loss in the highly hostile underwater environment. The reduced interference causes efficient utilization of the limited battery power of the sensor nodes that, in consequence, prolongs the entire network lifetime. In this paper, we propose an energy-efficient interference-aware routing (EEIAR) protocol for UWSNs. A sender node selects the best relay node in its neighborhood with the lowest depth and the least number of neighbors. Combination of the two routing metrics ensures that data packets are forwarded along the least interference paths to reach the final destination. The proposed work is unique in that it does not require the full dimensional localization information of sensor nodes and the network total depth is segmented to identify source, relay and neighbor nodes. Simulation results reveal better performance of the scheme than the counterparts DBR and EEDBR techniques in terms of energy efficiency, packet delivery ratio and end-to-end delay.
Data provenance is information used in reasoning about the present state of a data object, provid... more Data provenance is information used in reasoning about the present state of a data object, providing details such as the inputs used, transformations it underwent, entities responsible, and any other information that had an impact on its evolution. With a plethora of uses consisting of but not limited to provision of trust, gauging of quality, detecting intrusion and system changes, solving attribution problems, regulations compliance and in legal proceedings etc, provenance information needs to be secured. On the other hand use of tampered provenance information could lead to erroneous judgments and serious implications in many situations. The difference in sensitivity levels of provenance and the underlying data coupled with its DAG (Directed Acyclic Graph) structure leads to the need for a tailored security model. To date, proposed secure provenance schemes such as the Onion scheme, PKLC scheme, Mutual agreement scheme, rely on transitive trust; consecutive participating entities do not collude to attack the provenance chain. Furthermore, these schemes suffer from attacks such as ownership and lone attacks on provenance records. We propose a secure provenance scheme that uses the auditor as a witness to the chain build process whereby a verification tree is incrementally built by the auditor which serves as his view of the chain. Our scheme removes the transitive trust dependency hence collusion attacks by consecutive participating entities are successfully detected. Additionally, our scheme captures the DAG structure of provenance information and achieves secure provenance requirements; integrity, availability and confidentiality. Security analysis and empirical results show that the scheme provides better security guarantees than the previously proposed schemes with reasonable overheads involved that can be outweighed by the protection capabilities provided and removal of transitive trust which may not be feasible.
—In this letter, we discuss multiple links with equal weights, in buffer size based relay selecti... more —In this letter, we discuss multiple links with equal weights, in buffer size based relay selection schemes in cooperative wireless networks. A general relay selection factor is defined, which includes the weight of the link as the first metric and the link quality, or priority, as the second metric for different cases of the same weight. The Markov chain based theoretical framework is employed to evaluate the outage probability, delay and throughput of the system. The proposed scheme is evaluated for symmetric and asymmetric channel conditions. The link quality based second selection metric achieves lower outage probability , while the link priority based selection shows significant improvements in terms of delay and throughput. Theoretical results are validated through extensive Monte carlo simulations.
Internet of Things (IoT) enabled Smart grid (SG) is one of the most advanced technologies, which ... more Internet of Things (IoT) enabled Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management (DSM). Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms; harmony search algorithm (HSA), enhanced differential evolution (EDE) and harmony search differential evolution (HSDE) is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours.We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range which is bearable for consumer with respect to electricity bill. Feasible regions are defined to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak to average ratio and waiting time minimization.
Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long ... more Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.
Rapid increase in Internet worldwide users is leading a trend towards grand geographical distribu... more Rapid increase in Internet worldwide users is leading a trend towards grand geographical distributed systems. Content Distribution Networks (CDNs) are one of the popular large scale distributed systems managing around 40con-tribution in CO2 footprint, it is very important to manage resources efficiently and to control energy consumption in CDNs. Though different studies propose solutions to reduce energy consumption but there is less concentration on analyzing energy consumption and resource utilization. In this paper we have evaluated resource utilization and ultimately energy consumption under different scenarios. We have proposed utilization-aware energy consumption model. We have considered one of the most popular request redirection policy i.e. load-balance. Quality of Experience (QoE) metrics like overall delay in client requests completion and dropped requests are also evaluated. Extensive simulation is performed by varying CDN infrastructure size, client requests traffic volume and intensity of end user requests.
Demand side management (DSM) will play a significant role in the future smart grid by managing lo... more Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
Due to dynamic network topology, distributed architecture and absence of a centralized authority,... more Due to dynamic network topology, distributed architecture and absence of a centralized authority, Mobile Ad hoc Networks (MANETs) are vulnerable to various attacks from misbehaving nodes. To enhance the security of MANETs, various trust-based schemes have been proposed that augment the traditional cryptography-based security schemes. However, most of these schemes use static and predefined trust thresholds for node misbehavior detection, without taking into consideration the network conditions locally at each node. Using static trust thresholds for mis-behavior detection may result in high false positives, low malicious node detection rate, and network partitioning. In this paper, we propose a novel Adaptive Trust Threshold (ATT) strategy that adapts the trust threshold in the routing protocol according to the network conditions such as rate of link changes, node degree and connectivity, and average neighborhood trustworthiness. We identify the topology factors that affect the trust threshold at each node, and leverage them to build a mathematical model for ATT computation. We compare our ATT strategy with one of the most recently proposed trust-based security schemes. Our simulation results indicate that the ATT strategy is robust in terms of convergence to the same trust threshold value computed at all neighbor nodes for malicious nodes and is energy efficient. Moreover, the ATT strategy achieves significant improvements in packet delivery ratio, reduction in false positives, and increase in detection rate as compared to non-ATT schemes (static trust threshold based schemes).
Today's buildings are responsible for about 40% of total energy consumption and 30–40% of carbon ... more Today's buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
For energy-efficient resource management, void node avoidance is one of the key objectives in the... more For energy-efficient resource management, void node avoidance is one of the key objectives in the energy constrained underwater wireless sensor networks (UWSNs). In this paper, we propose two new routing protocols for the UWSN which is one of the end parts of a cloud. The first protocol is avoiding void node with adaptive hop-by-hop vector based forwarding (AVN-AHH-VBF), and the second is cooperation-based AVN-AHH-VBF (CoAVN-AHH-VBF). In both schemes, sensor nodes forward data packets in multi-hop fashion within a virtual pipeline. The nodes outside the pipeline do not forward data packets to avoid flooding in the network. At each hop, forwarding toward void region of the network is avoided by utilizing two hop information. Results of extensive simulations show that our proposed schemes significantly improve the network performance in terms of delivery ratio, energy expenditure and delay as compared with the selected existing scheme (AHH-VBF).
In this article, to monitor the fields with square and circular geometries, three energy-efficien... more In this article, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks. First one is sparsity-aware energy-efficient clustering, second one is circular sparsity-aware energy-efficient clustering, and the third one is circular depth–based sparsity-aware energy-efficient clustering routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions, whereas sparsity search algorithm is proposed to find sparse regions and density search algorithm is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet, while sink mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold (d th) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counterpart schemes (depth-based routing and energy-efficient depth-based routing) in terms of energy efficiency.
Smart grid is an emerging technology which is considered to be an ultimate solution to meet the i... more Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load man... more Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR). In this paper, we design a HEM controller on the basis of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed a hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction, as it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors ... more In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.
Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in ... more Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in Underwater Wireless Sensor Networks (UWSNs). In this paper, we present a Balanced Load Distribution (BLOAD) scheme to avoid energy holes created due to unbalanced energy consumption in UWSNs. Our proposed scheme prolongs the stability period and lifetime of the UWSNs. In BLOAD scheme, data (generated plus received) of underwater sensor nodes is divided into fractions. The transmission range of each sensor node is logically adjusted for evenly distributing the data fractions among the next hop neighbor nodes. Another distinct feature of BLOAD scheme is that each sensor node in the network sends a fraction of data directly to the sink by adjusting its transmission range and continuously reports data to the sink till its death even if an energy hole is created in its next hop region. We implement the BLOAD scheme, by varying the fractions of data using adjustable transmission ranges in homogeneous and heterogeneous simulation environments. Simulation results show that the BLOAD scheme outperforms the selected existing schemes in terms of stability period and network lifetime. INDEX TERMS Underwater wireless sensor networks, energy hole, balance load distribution, weight, energy balancing, stability.
Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefor... more Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefore, it is important to know the accurate position of the sensor node in large scale applications like disaster prevention, tactical surveillance, and monitoring. Due to the inefficiency of the global positioning system (GPS) in UWSN, it is very difficult to localize a node in underwater environment compared to terrestrial networks. To minimize the localization error and enhance the localization coverage of the network, two routing protocols are proposed; the first one is mobile autonomous underwater vehicle (MobiL-AUV) and the second one is cooperative MobiL (CO-MobiL). In MobiL-AUV, AUVs are deployed and equipped with GPS and act as reference nodes. These reference nodes are used to localize all the nonlocalized ordinary sensor nodes in order to reduce the localization error and maximize the network coverage. CO-MobiL is presented in order to improve the network throughput by using the maximal ratio combining (MRC) as diversity technique which combines both signals, received from the source and received from the relay at the destination. It uses amplify-and-forward (AF) mechanism to improve the signal between the source and the destination. To support our claims, extensive simulations are performed.
Cooperative routing is an appealing challenge in underwater wireless sensor networks (UWSNs). In ... more Cooperative routing is an appealing challenge in underwater wireless sensor networks (UWSNs). In this paper, we propose a region based cooperative routing protocol (RBCRP) for amplify and forward (AF) technique over Rayleigh faded channels in UWSNs. The source node sends the sensed signal to the destination and available relay nodes. At the destination node, bit error rate (BER) is checked on the basis of which, either positive or negative acknowledgement (ACK or NACK) is sent to the source and relay nodes. We also use mobile sinks (MSs) and energy harvesting techniques to further prolong the network lifetime and maximize the throughput. Our derived mathematical equations for the SNR gain and outage probability are verified by simulations. Results show that RBCRP performs better than the existing incremental best relay technique (IBRT) in terms of throughput, network lifetime and outage probability.
—Short-term load forecasting (STLF) models are very important for electric industry in the trade ... more —Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.
One of the major challenges in design of wireless sensor networks (WSNs) is to reduce energy cons... more One of the major challenges in design of wireless sensor networks (WSNs) is to reduce energy consumption of sensor nodes to prolong lifetime of finite capacity batteries. In this paper, we propose energy-efficient adaptive scheme for transmission (EAST) in WSNs. EAST is an IEEE 802.15.4 standard compliant. In this scheme, open-looping feedback process is used for temperature-aware link quality estimation and compensation, wherea closed-loop feedback process helps to divide network into three logical regions to minimize overhead of control packets. Threshold on transmitter power loss (RSSI loss) and current number of nodes (í µí± í µí± (í µí±¡)) in each region help to adapt transmit power level (í µí± level) according to link quality changes due to temperature variation. Evaluation of the proposed scheme is done by considering mobile sensor nodes and reference node both static and mobile. Simulation results show that the proposed scheme effectively adapts transmission í µí± level to changing link quality with less control packets overhead and energy consumption as compared to classical approach with single region in which maximum transmitter í µí± level assigned to compensate temperature variation.
Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one... more Interference-aware routing protocol design for underwater wireless sensor networks (UWSNs) is one of the key strategies in reducing packet loss in the highly hostile underwater environment. The reduced interference causes efficient utilization of the limited battery power of the sensor nodes that, in consequence, prolongs the entire network lifetime. In this paper, we propose an energy-efficient interference-aware routing (EEIAR) protocol for UWSNs. A sender node selects the best relay node in its neighborhood with the lowest depth and the least number of neighbors. Combination of the two routing metrics ensures that data packets are forwarded along the least interference paths to reach the final destination. The proposed work is unique in that it does not require the full dimensional localization information of sensor nodes and the network total depth is segmented to identify source, relay and neighbor nodes. Simulation results reveal better performance of the scheme than the counterparts DBR and EEDBR techniques in terms of energy efficiency, packet delivery ratio and end-to-end delay.
Data provenance is information used in reasoning about the present state of a data object, provid... more Data provenance is information used in reasoning about the present state of a data object, providing details such as the inputs used, transformations it underwent, entities responsible, and any other information that had an impact on its evolution. With a plethora of uses consisting of but not limited to provision of trust, gauging of quality, detecting intrusion and system changes, solving attribution problems, regulations compliance and in legal proceedings etc, provenance information needs to be secured. On the other hand use of tampered provenance information could lead to erroneous judgments and serious implications in many situations. The difference in sensitivity levels of provenance and the underlying data coupled with its DAG (Directed Acyclic Graph) structure leads to the need for a tailored security model. To date, proposed secure provenance schemes such as the Onion scheme, PKLC scheme, Mutual agreement scheme, rely on transitive trust; consecutive participating entities do not collude to attack the provenance chain. Furthermore, these schemes suffer from attacks such as ownership and lone attacks on provenance records. We propose a secure provenance scheme that uses the auditor as a witness to the chain build process whereby a verification tree is incrementally built by the auditor which serves as his view of the chain. Our scheme removes the transitive trust dependency hence collusion attacks by consecutive participating entities are successfully detected. Additionally, our scheme captures the DAG structure of provenance information and achieves secure provenance requirements; integrity, availability and confidentiality. Security analysis and empirical results show that the scheme provides better security guarantees than the previously proposed schemes with reasonable overheads involved that can be outweighed by the protection capabilities provided and removal of transitive trust which may not be feasible.
—In this letter, we discuss multiple links with equal weights, in buffer size based relay selecti... more —In this letter, we discuss multiple links with equal weights, in buffer size based relay selection schemes in cooperative wireless networks. A general relay selection factor is defined, which includes the weight of the link as the first metric and the link quality, or priority, as the second metric for different cases of the same weight. The Markov chain based theoretical framework is employed to evaluate the outage probability, delay and throughput of the system. The proposed scheme is evaluated for symmetric and asymmetric channel conditions. The link quality based second selection metric achieves lower outage probability , while the link priority based selection shows significant improvements in terms of delay and throughput. Theoretical results are validated through extensive Monte carlo simulations.
Internet of Things (IoT) enabled Smart grid (SG) is one of the most advanced technologies, which ... more Internet of Things (IoT) enabled Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management (DSM). Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms; harmony search algorithm (HSA), enhanced differential evolution (EDE) and harmony search differential evolution (HSDE) is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours.We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range which is bearable for consumer with respect to electricity bill. Feasible regions are defined to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak to average ratio and waiting time minimization.
Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long ... more Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.
Rapid increase in Internet worldwide users is leading a trend towards grand geographical distribu... more Rapid increase in Internet worldwide users is leading a trend towards grand geographical distributed systems. Content Distribution Networks (CDNs) are one of the popular large scale distributed systems managing around 40con-tribution in CO2 footprint, it is very important to manage resources efficiently and to control energy consumption in CDNs. Though different studies propose solutions to reduce energy consumption but there is less concentration on analyzing energy consumption and resource utilization. In this paper we have evaluated resource utilization and ultimately energy consumption under different scenarios. We have proposed utilization-aware energy consumption model. We have considered one of the most popular request redirection policy i.e. load-balance. Quality of Experience (QoE) metrics like overall delay in client requests completion and dropped requests are also evaluated. Extensive simulation is performed by varying CDN infrastructure size, client requests traffic volume and intensity of end user requests.
Demand side management (DSM) will play a significant role in the future smart grid by managing lo... more Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
Due to dynamic network topology, distributed architecture and absence of a centralized authority,... more Due to dynamic network topology, distributed architecture and absence of a centralized authority, Mobile Ad hoc Networks (MANETs) are vulnerable to various attacks from misbehaving nodes. To enhance the security of MANETs, various trust-based schemes have been proposed that augment the traditional cryptography-based security schemes. However, most of these schemes use static and predefined trust thresholds for node misbehavior detection, without taking into consideration the network conditions locally at each node. Using static trust thresholds for mis-behavior detection may result in high false positives, low malicious node detection rate, and network partitioning. In this paper, we propose a novel Adaptive Trust Threshold (ATT) strategy that adapts the trust threshold in the routing protocol according to the network conditions such as rate of link changes, node degree and connectivity, and average neighborhood trustworthiness. We identify the topology factors that affect the trust threshold at each node, and leverage them to build a mathematical model for ATT computation. We compare our ATT strategy with one of the most recently proposed trust-based security schemes. Our simulation results indicate that the ATT strategy is robust in terms of convergence to the same trust threshold value computed at all neighbor nodes for malicious nodes and is energy efficient. Moreover, the ATT strategy achieves significant improvements in packet delivery ratio, reduction in false positives, and increase in detection rate as compared to non-ATT schemes (static trust threshold based schemes).
Today's buildings are responsible for about 40% of total energy consumption and 30–40% of carbon ... more Today's buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for... more SUMMARY In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at 'time of use' and 'state of charge' of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of 'no profit, no loss'. The objective of the proposed PDH is to limit the purchase of electricity on 'high priced' hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of 'sharing power economy' for end users and efficient use of power within the smart community.
For energy-efficient resource management, void node avoidance is one of the key objectives in the... more For energy-efficient resource management, void node avoidance is one of the key objectives in the energy constrained underwater wireless sensor networks (UWSNs). In this paper, we propose two new routing protocols for the UWSN which is one of the end parts of a cloud. The first protocol is avoiding void node with adaptive hop-by-hop vector based forwarding (AVN-AHH-VBF), and the second is cooperation-based AVN-AHH-VBF (CoAVN-AHH-VBF). In both schemes, sensor nodes forward data packets in multi-hop fashion within a virtual pipeline. The nodes outside the pipeline do not forward data packets to avoid flooding in the network. At each hop, forwarding toward void region of the network is avoided by utilizing two hop information. Results of extensive simulations show that our proposed schemes significantly improve the network performance in terms of delivery ratio, energy expenditure and delay as compared with the selected existing scheme (AHH-VBF).
In this article, to monitor the fields with square and circular geometries, three energy-efficien... more In this article, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks. First one is sparsity-aware energy-efficient clustering, second one is circular sparsity-aware energy-efficient clustering, and the third one is circular depth–based sparsity-aware energy-efficient clustering routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions, whereas sparsity search algorithm is proposed to find sparse regions and density search algorithm is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet, while sink mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold (d th) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counterpart schemes (depth-based routing and energy-efficient depth-based routing) in terms of energy efficiency.
Smart grid is an emerging technology which is considered to be an ultimate solution to meet the i... more Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load man... more Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR). In this paper, we design a HEM controller on the basis of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed a hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction, as it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors ... more In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.
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Papers by Nadeem Javaid