Wireless sensor networks (WSNs) are one of the very active research area. They have many applications like military, health care, environmental monitoring and industrial monitoring. The sensor nodes have limited energy source. Since in many cases the nodes are deployed in unreachable areas, hence recharging or replacing the battery of the sensor nodes is not an option. Therefore, one must employ techniques to conserve the energy by reducing the energy consumption by the nodes. In this paper, we discuss about the different energy saving schemes investigated by different research community in WSNs to reduce the energy consumption of the nodes and thereby improving the lifetime of the overall network. Energy saving protocols such as duty cycle, energy efficient routing, energy efficient medium access control (MAC), data aggregation, cross layer design and error control code (ECC) are discussed. Sleep/wake up method is adopted by the duty cycle approach to reduce the active time of the nodes and conserve their energy. The routing and MAC protocols use suitable energy efficient algorithms for saving energy. The data aggregation aims to save energy by reducing the number of transmissions. On the other side, cross layer approach looks for a cross layer optimization solution to improve the energy efficiency of the network. ECC reduces energy consumption by virtue of coding gain it offers which allows lower signal-to-noise ratio (SNR) to achieve the same bit error rate (BER) as an uncoded system. Some techniques such as use of directional antennas, topology control and transmission power control which have been widely investigated for other ad-hoc networks for energy conservation are also discussed in brief in this paper.

Different Energy Saving Schemes in Wireless Sensor Networks: A Survey

Mobile Agent (MA) paradigm for data aggregation in wireless sensor networks (WSNs) presents a distributed computing paradigm which has proved its efficiency in comparison to the traditional client–server computing paradigm. In terms of energy consumption and overall time response, MA computing paradigm presents a better alternative. Instead of sending the collected data to the sink as in client/server, MA moves to sensor nodes (SNs) for data collection. For MA, to move among SNs, an itinerary should be planned before the migration. Many approaches have been proposed to solve the problem, but all approaches did not take into consideration the fault tolerance problem, even though WSNs are prone to failure. In this respect, we propose Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks (EFTA) to plan the itinerary for MA and another alternative itinerary in case of node(s) failure. Our algorithm adopts a clustering method to group SNs in clusters then plans the itinerary among those clusters efficiently. Also an alternative itinerary based fault tolerance is proposed. Simulation results show that our algorithm performs better than other existing ones.

Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Cluster based routing strategies are popular categories of routing protocols among wireless sensor networks (WSNs). In a typical cluster based routing protocols, few nodes are elected as cluster Head (CH) nodes and they form clusters with other nodes of the networks known as cluster members. The data sensed by cluster members is sent to CH node for data aggregation and further processing. Data Aggregation is considered to be one of the important methods used to prolong the network lifetime of WSN. Data aggregation is quite popular among cluster based routing protocols for WSN, where the data packets are aggregated by intermediate nodes till it reaches to BS. Data aggregation ultimately helps in reducing the number of data messages thereby assisting in network life prolongation. In this paper, an intra-cluster data aggregation technique (ICA) for WSNs is proposed. ICA constructs the intra cluster data aggregation path from a source node to its CH node in an energy efficient way. The data packets are aggregated along the aggregation path by intermediate relay nodes till the message reaches to designated CH node. Comparative analysis of ICA, LEACH and LEACH-C protocols are carried by considering the parameters such as number of alive nodes, energy consumption, number of data packets received by BS. Results obtained show that ICA performs better compared to LEACH and LEACH-C protocols.

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Mobile agent (MA)-based wireless sensor networks present a good alternative to the traditional client/server paradigm. Instead of sending the data gathered by each node to the sink as in client/server, MAs migrate to the sensor nodes (SNs) to collect data, thus reducing energy consumption and bandwidth usage. For MAs, to migrate among SNs, an itinerary should be planned before the migration. Many approaches have been proposed to solve the problem of itinerary planning for MAs, but all of these approaches are based on the assumption that MAs visit all SNs. This assumption, however, is inefficient because of the increasing size of the MAs after visiting each node. Also, in case of node(s) failure, as it is often the case in WSNs, the MAs may not be able to migrate among SNs. None of the proposed approaches takes into consideration the problem of fault tolerance. In this paper, we propose multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks (MAEF) to plan itineraries for MAs. This can be achieved by grouping nodes in clusters and planning itineraries efficiently among cluster heads (CHs) only. What is more, an alternative itinerary is planned in case of node(s) failure. The simulation result clearly shows that our novel approach performs better than the existing ones.

/pdf/multi-mobile-agent-itinerary-planning-based-energy-and-fault-4oegh2d3fy.pdf

Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks

Energy efficiency have always been a priority while designing wireless sensor networks. Introduction of mobile agent technology in wireless sensor networks for collaborative signal and information processing has provided the new scope for efficient processing and aggregation of data. Mobile agent based distributed computing paradigm offers numerous benefits over the existing and commonly used client/server computing paradigm in wireless sensor networks. Mobile agent performs the task of data processing and data aggregation at the node level rather than at the sink, thus, eliminating the redundant network overhead. One of the most important issues in mobile agent based paradigm is planning of an itinerary for agent traversal. In this paper, we have proposed a dynamic mobile agent based data aggregation approach that takes into consideration energy efficiency, network lifetime, end to end delay and aggregation ration while making the decision for migration of agent in multihop sensor network. As our approach focuses on finding the most informative route by traversing comparatively less number of nodes consequently mobile agent takes less time to return to processing element, thus, exhibiting lower delay.

Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network

Mobile Agent (MA) based distributed computing paradigm offers numerous benefits over the existing and commonly used client/server computing paradigm in wireless sensor networks (WSN). MA performs the task of data processing and data aggregation at the node level rather than at the sink, thus, eliminating the redundancy network overhead. One of the most important issues in MA based paradigm is planning of an itinerary (route) for MA traversal. In this paper, we present a centralized MA itinerary planning approach using Grey Relational Analysis (GRA). The residual energy, migration cost and information gain are the factors considered for the next node selection of the itinerary, and GRA is used for finding out their importance level and determine their weighted values. Simulation results exhibit that the GRA based proposed approach performs better than the previously existing approaches.

Itinerary planning using grey relational analysis for mobile agent based wireless sensor networks

Mobile Agent (MA) based computing paradigm offers many advantages over the existing and widely used client/server computing paradigm in wireless sensor networks (WSN). MA performs the task of data processing and data aggregation at the nodes rather than at the sink, thus, eliminating the redundancy in the sensory data. One of the most important issues in MA based paradigm is planning of an itinerary (route) for MA traversal. In this paper, we present a centralized MA itinerary planning approach using Analytic Hierarchy Process (AHP). The remaining energy of the node, migration cost and information gain are taken into consideration for the selection of subsequent node of the itinerary. AHP is used for determining the weights to each of the above parameter, thus, simplifying the task of decision making. Simulation results exhibit that the proposed approach lowers the task duration and energy consumption of the network.

Divya Lohani

Papers

Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network

Itinerary planning using grey relational analysis for mobile agent based wireless sensor networks

Mobile agent itinerary planning using analytic hierarchy process for wireless sensor networks