Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach

Clustering has been proven to be one of the most efficient techniques for saving energy of wireless sensor networks (WSNs). However, in a hierarchical cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving and aggregating the data from their member sensor nodes and transmitting the aggregated data to the base station. Therefore, the proper selection of CHs plays vital role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient cluster head selection algorithm which is based on particle swarm optimization (PSO) called PSO-ECHS. The algorithm is developed with an efficient scheme of particle encoding and fitness function. For the energy efficiency of the proposed PSO approach, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes. We also present cluster formation in which non-cluster head sensor nodes join their CHs based on derived weight function. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The results are compared with some existing algorithms to demonstrate the superiority of the proposed algorithm.

A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks

Expanding Internet-based services are driving the need for evermore bandwidth in the network backbone These needs will grow further as new real-time multimedia applications become more feasible and pervasive Currently, there is no other technology on the horizon that can effectively meet such a demand for bandwidth in the transport infrastructure other than WDM technology This technology enables incremental and quick provisioning up to and beyond two orders of magnitude of today's fiber bandwidth levels This precludes the need to deploy additional cabling and having to contend with right-of-way issues, a key advantage Hence, it is only natural that over time optical/WDM technology will migrate closer to the end users, from core to regional, metropolitan, and ultimately access networks At present, WDM deployment is mostly point-to-point and uses SONET/SDH as the standard layer for interfacing to the higher layers of the protocol stack However, large-scale efforts are underway to develop standards and products that will eliminate one or more of these intermediate layers (eg, SONET/SDH, ATM) and run IP directly over the WDM layer IP over WDM has been envisioned as the winning combination due to the ability of the IP to be the common revenue-generating convergence sublayer and WDM as a bandwidth-rich transport sublayer Various important concerns still need to be addressed regarding IP-WDM integration These include lightpath routing coupled with tighter interworkings with IP routing and resource management protocols, survivability provisioning, framing/monitoring solutions, and others

On IP-over-WDM integration

Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.

An Energy-Efficient Clustering Solution for Wireless Sensor Networks

Higher power relay nodes can be used as cluster heads in two-tiered sensor networks to achieve improved network lifetime. The relay nodes may form a network among themselves to route data towards the base station. In this model, the lifetime of a network is determined mainly by the lifetimes of these relay nodes. An energy-aware communication strategy can greatly extend the lifetime of such networks. However, integer linear program (ILP) formulations for optimal, energy-aware routing quickly become computationally intractable and are not suitable for practical networks. In this paper, we have proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. Furthermore, our algorithm can easily handle large networks, where it leads to significant improvements compared to traditional routing schemes.

A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

Clustering strategies for improving the lifetime of two-tiered sensor networks

Since the optical reach (the distance an optical signal can travel before its quality degrades to a level that necessitates regeneration) ranges from 500 to 2000 miles, regeneration of optical signals is essential to establish lightpaths of lengths greater than the optical reach. In a translucent optical network, the optical signal is regenerated at selected nodes of the network before the signal quality degrades below a threshold. Given the optical reach of the signal, to minimize the overall network design cost, the goal of the regenerator placement problem is to find the minimum number of regenerators necessary in the network, so that every pair of nodes is able to establish a lightpath (either transparent or translucent) between them. In this paper, we study the regenerator placement problem and prove that the problem is NP-complete. We formulate the regenerator placement problem as a connected dominating set problem in a labeled graph (LCDS) and provide a procedure for computing it. We evaluate the effectiveness of our approach using a number of networks.

On Sparse Placement of Regenerator Nodes in Translucent Optical Network

Nodes in sensor networks are often prone to failure, particularly when deployed in hostile territories, where chances of damage/destruction are significantly higher. There is also the possibility for the loss of connectivity between nodes due to the inherent limitations of the wireless communication medium. Therefore, a sensor network should be designed in such a way that the network is able to continue to operate, even if some of the nodes/links in the network fail. The scalability and the lifetime of sensor networks are affected by the limited transmission range and the battery power of sensor nodes. Recently, relay nodes have been proposed for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher-powered relay nodes can be used as cluster heads. Finding the minimum number of such relay nodes, along with their locations, so that each sensor node can communicate with at least k s (k s = 1, 2...) relay nodes and the relay node network is k r -connected (k r = 1, 2...), is known to be a difficult problem. Some recent works in this area have proposed heuristic solutions for the the special cases of k s = 1 or 2 and k r = 1 or 2. In this paper, we have presented a generalized integer linear program (ILP) formulation capable of generating exact solutions for arbitrary values of k s and k r .

Optimal Placement of Relay Nodes in Two-Tiered, Fault Tolerant Sensor Networks

Recent technological advances in the field of micro-electroc-mechanical systems (MEMS) have made the development of multi-functional sensor nodes technically and economically feasible. The lifetime of sensor networks is still limited as individual sensor nodes are usually powered by battery. In the past few years, the use of relay nodes in sensor networks has been proposed in literature for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks, higher-powered relay nodes can also be used as cluster heads. These relay nodes may form a network among themselves and route data towards the base station. In such a sensor network, the lifetime of the network is directly related to the lifetime of these relay nodes. In this paper, we have proposed an ILP solution for scheduling the data gathering of relay nodes such that the lifetime of the relay node network is maximized. We have compared our formulation with the direct transmit energy model and shown that it can lead to significant improvements. We have also proposed a re-scheduling approach which can further extend the maximized lifetime of the network.

Subir Bandyopadhyay

Papers

A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

Clustering strategies for improving the lifetime of two-tiered sensor networks

On Sparse Placement of Regenerator Nodes in Translucent Optical Network

Optimal Placement of Relay Nodes in Two-Tiered, Fault Tolerant Sensor Networks

Maximizing the Lifetime of Two-Tiered Sensor Networks