Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Graphical abstractDisplay Omitted HighlightsPropose two computationally effective range-free (RF) 3D node localization schemes using applications of biogeography based optimization (BBO) and hybrid particle swarm optimization (HPSO) for anisotropic wireless sensor networks.Nodes are randomly deployed with constraints over three layer boundaries. The anchor nodes are randomly distributed over top layer only and target nodes are distributed over the middle and bottom layers.Non-linearity between received signal strength (RSS) and distance is modeled using fuzzy logic system (FLS) to reduce the computational complexity and further optimized by HPSO and BBO to minimize the error.Knowledge based edge weight of the anchor node to determine the accurate coordinates of the target node.A novel proximity based performance index, to evaluate the proposed schemes. In this paper, we propose two computationally efficient 'range-free' 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.

Range-free 3D node localization in anisotropic wireless sensor networks

Mobile robots can provide a wide range of valueadded services to wireless sensor networks during their operational lifetime One of them has to do with the replacement of damaged sensors with other functional, passive ones This scenario has been recently studied as the “Robot-Assisted Sensor Relocation” (RASR) problem RASR solutions traditionally focus only on one aspect of the problem: minimizing the total distance traveled by the robotThe existing centralized solutions do not take into account the reliability of the passive nodes that are selected as substitutes for the damaged nodes, for instance, their current battery level With this in mind, we propose a multi-objective optimization (MOO) formulation of the problem, named Reliable Robot-Assisted Sensor Relocation (RRASR), where we consider two more objectives in addition to the trajectory length These objectives result from the fact that a given passive sensor selected to replace a damaged sensor in the region may not be in perfect condition and that another passive sensor may be a better option Due to the nature of MOO problems, we must present a diverse set of solutions that exhibit a trade-off among the different decision objectives to a network manager so they may take appropriate action We evaluate the performance of four state-of-the-art evolutionary MOO algorithms with sensor networks of varying sizes and inflicted damage levels To the best of our knowledge, this is the first time RASR is approached from an MOO angle

A multi-objective optimization approach to Reliable Robot-Assisted Sensor Relocation

Wireless sensor networks provide a method for monitoring a region of interest. Incorporating a mobile robot within the sensor network allows various types of functionality to be added. One example of this is the replacement of risky and/or damaged sensors with other functional, passive ones. Using a specially designed risk management framework (RMF), we can proactively detect sensors that are at a high risk for failure and replace them before any network coverage is lost. The problem of optimizing the robot trajectory while picking up passive sensors and dropping them at the locations of the damaged sensors in the field has been studied as the “Robot-Assisted Sensor Relocation” (RASR) problem. One shortcoming of existing RASR methods is that the chosen robot trajectory is the one with the shortest length; however, no regards as to the durability of the passive sensors in the relocation chain are taken into consideration. We propose a more robust manner to come up with these trajectories by taking into account the current energy levels of the participating passive sensors as well as the ideal locations for their deployment. We resort to multi-objective optimization (MOO) to handle the tradeoffs among the different decision objectives that are part of this new formulation, named here as “Reliable Robot-Assisted Sensor Relocation”. We outline the RRASR problem as well as the RMF used for detecting risky sensors in the wireless sensor network before the calculation of the sensor relocation trajectory takes place. We also evaluate the performance of six state-of-the-art evolutionary multi-objective optimization (EMOO) algorithms with sensor networks of varying sizes, inflicted damage levels, and passive sensor densities. The empirical results confirm the feasibility of utilizing EMOO approaches to suggest multiple sensor relocation trajectories to the network manager.

Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-objective Optimization

A rescue robot guided by visual sensors can not find its path and target in the environment full of smoke or fog. It makes the danger disposal task of rescue robot difficult, especially the task of pipeline leakage disposal in a chemical factory. In this paper, a rescue robot guided by audio sensors is presented. As audio signals can bypass obstacles and remedy the shortcomings of visual and other sensors, it can guide the robot to the sound source according to the difference of sound field strength between two audio sensors. And band-pass filters are equipped alone the audio sensor input ports for tracing the special frequency sound signals emitted by the pipeline leakage. Besides, touch sensors are designed for the obstacle avoidance. Some typical experiments support the effects of approach in this paper.

An Audio Guided Resource Robot Based on Band-pass Filter and Touch Sensors

A standard traveling salesman problem(TSP) under dual-objective strategy constrained is proposed in this paper, characterized by the fact that the demand of both as many as possible the numbers of nodes be visited in time and minimum trajectory distance. The motivation for this TSP problem under dual-objective strategy constrain stems from the coverage repair strategies for wireless sensor networks using mobile actor based on energy analysis, wherein a mobile robot replenishes sensors energy when it reaches the sensor node location. The Evolutionary Algorithm (EA) meta-heuristic elegantly solves this problem by the reasonable designed operators of crossover, mutation and local search strategy,which can accelerate convergence of the optimal solution. The global convergence of the proposed algorithm is proved, and the simulation results show the effectiveness of the proposed algorithm.

https://www.beei.org/index.php/EEI/article/download/285/191

Ye Miao

Papers

Coverage Repair Strategies for Wireless Sensor Networks using Mobile Actor Based on Evolutionary Computing