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In this paper, an adjacent coronas based network architecture in which nodes are deployed in accordance with a probability density function (PDF) is proposed. The intrinsic characteristics of the PDF with optimal placement and number of nodes within a corona are determined. The conditions for an energy balanced network are also derived analytically. To confirm the theoretical findings, simulation was carried out in two phases. In the first phase, nodes are deployed as per proposed algorithm and the performance of proposed PDF is compared with that of the other existing distribution techniques. Results of the first phase confirm a significant improvement of up to 83.16% in the average network lifetime with better coverage and connectivity. In the second phase, initially the clustering protocol LEACH is suitably changed and thereafter, the proposed pdf and other existing deployment techniques are executed with LEACH to examine the effectiveness of the proposed algorithm on clustering. Results of the second phase confirm that the proposed algorithm enhances the time to die of the first node up to 16.54% as compared to other existing techniques. Simulation results also confirm that it is possible to obtain energy efficient node distribution in constant area adjacent coronas. [ABSTRACT FROM AUTHOR]

The application of swarm intelligence algorithms to wireless sensor networks (WSNs) deployment has been the focus of research community for past few years. One such algorithm is ant colony optimization (ACO), whose application in reducing the cost of WSNs in terms of deployed sensor nodes has recently attracted attention of the researchers. In this letter, we propose an ACO-based framework for WSN deployment in a realistic 3-D environment, by making modifications to the standard ACO algorithm. The simulation results lead to the conclusion that the proposed framework achieves better performance compared with the state-of-the-art ACO-based algorithms in terms of size of the solution for node deployment. In addition, in a 3-D environment, time overhead problem arises in standard ACO-based algorithms since they require a large number of iterations to achieve better solutions. In contrast, the performance of the proposed approach does not degrade with reduction in number of iterations, which enables the algorithm to achieve quick convergence. [ABSTRACT FROM AUTHOR]

Farmland environment has influence on the signal propagation of wireless sensor network (WSN). The network coverage and connectivity performances largely depend on the deployment method. In order to prolong the network lifespan, renewable energy was introduced to a part of the network nodes due to the cost issue. An improved energy aware deployment method is proposed in this paper. The aim of the paper is to maximize the network lifespan, lower the network cots, increase the energy utilization renewable energy nodes and balance the residual energy of the battery-power nodes. An energy consumption model was established in the consideration of deployment constraints over network coverage, connectivity, and stability. An improved particle swarm optimization (PSO) algorithm was designed to optimize the performance of the network model. A penalty function was included to resolve the problem which contain both continuous and discrete variables. The number of renewable nodes with the best cost-lifespan ratio was concluded. The simulation results demonstrate that the propose method increases the renewable energy utilization when the renewable energy source is good. Also the network keeps stable when the renewable energy source is off. The overall energy consumption of battery nodes were reduced and balanced. Therefore, the proposed deploy method prolong the network lifespan. [ABSTRACT FROM AUTHOR]

Ease of use, high flexibility and variety of applications have made wireless sensor networks very popular. Node placement in a sensor network is very critical since it affects important network attributes such as coverage, lifetime, and reliability. Therefore, controlled node placement is necessary for achieving specific network features with minimum number of nodes. Since node placement is an NP-hard problem, many placement algorithms have been proposed based on heuristic and meta-heuristic methods. Most of those algorithms assume a uniform event pattern (UEP) throughout the area under investigation. However, in practice some networks deal with non-uniform event pattern (NEP). Optimization of one attribute of the network usually results in degradation of other attributes. That is why in recent studies multi-objective optimization methods have been used which usually provide a set of best answers while the final answer is selected by a decision maker applying a trade-off between all attributes. In this paper a method for controlled node placement is proposed based on multi-objective optimization (MOO) algorithms considering a non-uniform event pattern for the network (NPNEP). In that sense this method is an extension of the previous methods that use uniform event pattern. Because of the good performance of multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), it is utilized as the optimization tool in node placement. In order to use MOEA/D, a few objective functions are defined which can optimize important attributes like coverage, power consumption, delay, reliability and lifetime. In order to achieve optimal node utilization, load balance, and increased lifetime, a cost function for routing is proposed as well as a data gathering and reporting method which both help increase the lifetime of a network. The proposed method can be used for wireless sensor networks with heterogeneous nodes. This algorithm can be used in initial deployment phase as well as operation phase or when problems like fragmentation or loss of coverage occurs. The result of NPNEP algorithm is the initial position of nodes or position of the new nodes which provide the best answers in MOO algorithm. Performance of the proposed algorithm and the effect of NEP as opposed to UEP have been verified by simulations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks on mountain roads will form winding and long transmission paths and accordingly result in centralized energy dissipation near the sink and large transmission delay. In this letter, we propose a node deployment scheme to settle the above-mentioned thorny issues. The core idea is to construct extra paths to the sink and achieve data diversion. Specifically, the places with relatively large transmission hops to the sink and also comparatively small distance away from it are selected as the starting points of the extra paths. This selection mechanism contributes to alleviating the energy holes and reducing the transmission delay. Finally, experimental simulations show the effectiveness and superiority of our deployment scheme. [ABSTRACT FROM PUBLISHER]

Longwall mining is extensively practiced worldwide for the safe extraction of coal from underground coal mines. In this paper, we analyzed several sensor node deployment schemes and proposed a novel wireless sensor network (WSN) deployment scheme for environmental monitoring in longwall coal mines. The proposed scheme is based on the probabilistic event detection approach for complete coverage of the monitoring regions in longwall coal mines. The virtual force method is applied to minimize the redundant overlapping regions of the sensor nodes by preserving connectivity of the network. Moreover, the funneling effect that causes energy hole problem in a linear wireless sensor network is resolved. The performance of the WSN is evaluated through simulations. The simulation results showed that the proposed scheme provides best area coverage as compared to the existing node deployment strategies and is very cost effective. [ABSTRACT FROM AUTHOR]

In the process of data collection for wireless sensor networks based on large-area multiple nodes, the utilization rate and energy efficiency of existing methods of nodes deployed in the network are relatively low. Therefore we propose hybrid layer-cluster topology method for large multi-node deployment in wireless sensor network in this paper. Data acquisition for each region is assigned to each cluster which is composed of cluster head node and ordinary nodes divided by the hexagon deployment strategy. Star-structure network is adopted between ordinary nodes and cluster head node, each cluster is connected by mesh-structure network. On this basis, the hybrid hop routing method is used to communicate with base station based on critical distance, which saves the cost and improves the efficiency of the network perception. The experimental results show that the layer cluster hybrid topology model based on hexagon partition can reach the coverage rate of 100 % with the energy utilization rate increasing by 5.4 and 8.6 % on the condition of relatively few deployed sensor nodes, compared to quadrilateral partition and random deployment model. This method overcomes the disproportion of the energy consumption and improves the information redundancy in the process of transmission, and can cover the maximum working area along with the longest survival time of the network by reducing energy consumption. This method is an effective solution to the problem of node deployment of large-scale sensor nodes in wireless sensor networks. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics. [ABSTRACT FROM PUBLISHER]

This paper studies the network deployment problem for an underwater sensor network consisting of anchored nodes, which are first randomly deployed onto the ocean surface and can move only along the z -axis direction. The objective is sink these surface nodes to appropriate depths so as to maximize the three dimensional coverage under the premise of ensuring the underwater network connectivity. Our solution consists of three stages: First, based on an efficient three dimensional sphere packing pattern, we select the first batch of sinking nodes on the surface to sink toward the ideal placement locations. The selection exploits a minimum cost perfect matching of a weighted complete bipartite graph to minimize the total deviation distance. Second, we check the connectivity of the sank nodes. If they cannot form a connected network, we propose two algorithms to restore network connectivity for the underwater network. Third, the remaining surface nodes, if any, are further sank to fix coverage holes by those already sank nodes, while guaranteeing their connectivity to the underwater network. Simulation results show that compared with an optimal solution based on exhaustive search, our algorithm can achieve comparable coverage ratio, while ensuring network connectivity with significantly reduced time complexity; compared with other peer algorithms ensuring network connectivity, our algorithm can achieve higher coverage ratio. [ABSTRACT FROM AUTHOR]

Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks (WSNs) are receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, preserving energy requires utmost attention, as they are highly resource constrained. One fundamental way of conserving energy is judicious deployment of nodes within the network for balancing energy flow throughout the network. Node deployment using Gaussian distribution is a standard practice and is widely acceptable when random deployment is used. Initially, an analysis is done to establish that Gaussian distribution based node deployment is not energy balanced. Standard deviation of Gaussian distribution is identified as the parameter responsible for energy balancing. A deployment strategy is proposed for energy balancing using customized Gaussian distribution by discretizing the standard deviation. Performance of the scheme is evaluated in terms of energy balance and network lifetime. Exhaustive simulation is performed to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing. The simulation results show that our scheme also provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with three competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) plays an important role in the large scale farmland environmental monitoring. The complex farmland environment has a great impact on the WSN performance. Extremely low power consumption of WSN is required because of the long monitoring period and limited energy. In the considering of network coverage, connectivity, organization and power consumption, this paper proposes a new deployment strategy in the consideration of solar power nodes. A mixed deployment method combining with structure and random filling deployment is used. The hot-spot nodes in the network are replaced by solar nodes in order to get a longer lifetime. The simulation results show that the new method has a better performance than the traditional algorithms. [ABSTRACT FROM AUTHOR]

For node deployment of heterogeneous sensor networks, based on different probability sensing models of heterogeneous nodes and an organic small molecule model, a 'class-molecule' sensing model of heterogeneous nodes is proposed. Combining it with DSmT data fusion model, the change of network coverage ratio after using the new sensing model and data fusion algorithm is studied. Furthermore, according to the research results, the node-deployment strategy of heterogeneous sensor networks based on organic small molecule model(NHOS) is proposed. Sensor network simulation model is formulated using MATLAB, the results show the effectiveness of the algorithm, where the coverage of network and detection efficiency of nodes are improved, the lifetime of network is prolonged, energy consumption and the number of deployment nodes is reduced, and the scope of rceiving is expanded. As a result, NHOS can improve the detection performance of the network. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change. [ABSTRACT FROM AUTHOR]

In target based wireless sensor networks (WSNs), coverage and connectivity are the two most important issues for guaranteed data forwarding from each target to a remote base station (BS). Given a set of target points, finding minimum number of potential positions to place sensor nodes fulfilling both coverage and connectivity is an NP-complete problem. In this paper, we propose a genetic algorithm (GA) based scheme to solve this problem. Keeping in mind that the sensor nodes are prone to failure, the proposed scheme provides k -coverage to all targets and m -connectivity to each sensor node. Our GA based approach is presented with efficient chromosome representation, derivation of efficient fitness function along with the usual GA operators. The scheme is simulated extensively with various scenarios of WSN. The simulation results are compared with some related existing algorithms to demonstrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

Based on analyzing deployment and coverage model of existing WSN node, and in view of disadvantages of difficulty in full coverage, high cost and node redundancy, the paper proposed a scheme of underground WSN coverage model and node deployment with configurable blind area, and introduced its application in mine underground. The deployment scheme of underground WSN node can adjust blind area scale according to actual condition of monitored object and realize blind area setting. The analysis result shows that the scheme covers larger area with the same quantity of nodes as full coverage; in the same monitoring area, the scheme needs less sensor nodes, not only saving system cost but also improving node redundancy problem in certain degree.

This paper searches for a deployment method with minimum sensor nodes to achieve agricultural intelligent monitoring. Because of the various features, topographies and complicated weather conditions, the selection of the location for deploying sensor nodes is difficult, especially in hilly areas. This paper puts forward a sensor node deployment algorithm for agricultural intelligent monitoring based on an optimized theory. It first develops a mathematical model for the node location selection. During the solution process, the features of the locations are of primary concern, as a location with more features is easier to choose than other locations. Next, a theoretical analysis and experimental simulation are carried out. Finally, OASNDFA is used in 3000 mu orange orchards in China. The result demonstrates that OASNDFA could perform relevant monitoring tasks with fewer monitoring nodes. [ABSTRACT FROM AUTHOR]

For wireless sensor networks, monitoring large inaccessible areas where deterministic node deployment is not possible, self-organized techniques are in demand to cover an area using optimal number of nodes. In this paper, given an initial random deployment of mobile sensor nodes, we propose a simple and novel technique for self-organized node movement to satisfy the coverage of the given region of interest using a least number of nodes, such that the maximum node displacement is minimized. We present a simple centralized algorithm and also a distributed version of it for node placement. Moreover, in case of a node failure, a distributed fault recovery algorithm is proposed to replace it locally utilizing the available free nodes. Analysis, simulation, and comparison studies show that the proposed algorithms with less neighborhood information result in significant improvement in terms of average and maximum displacement of a node, rounds of communication, and number of active nodes. [ABSTRACT FROM AUTHOR]

The energy consumption in wireless sensor networks is a significant matter and there are many ways to conserve energy. The use of mobile sensors is of great relevance to minimize the total energy dissipation in communication and overhead control packets. In a WSN, sensor nodes deliver sensed data back to the sink via multi hopping. The sensor nodes near the sink will usually consume more battery power than others; consequently, these nodes will quickly drain out their battery energy and decrease in the network lifetime of the WSN. The presence of mobile sinks causes increased energy reduction in their proximity, due to more relay load under multi hop communication. Moreover, node deployment technique can also be used to improve the life time of the network. Performance comparisons have been done by simulations between different routing protocols and our approach show efficient results. [ABSTRACT FROM AUTHOR]

For random deployment of wireless sensor networks in a specified geographical location and in the presence of obstacles, optimal network coverage is highly desirable while maintaining network connectivity. In this piece of work, we propose an efficient autonomous deployment scheme, named as Obstacle Avoidance Virtual Force Algorithm (OAVFA), for self-deployment of randomly scattered homogeneous as wells as heterogeneous mobile sensor nodes over a squared sensing field to enhance the network coverage and ensure the network connectivity in the presence of obstacles. Our proposed approach is localized in the sense that each decision taken by the sensor node is strictly based on information acquired from its neighbors. The simulation results show that OAVFA provides an efficient self-deployment of mobile sensor nodes in the presence of obstacles. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks in volatile environments may suffer damage which partitions the network, and connectivity must be restored. We investigate the online problem, in which the repairing agent must discover surviving nodes and the damage to the physical and radio environment as it moves around the sensor field to execute the repair. The objectives include minimising the cost of the repair in terms of new radios and distance travelled, and minimising the time to complete the repair. We consider a number of different agent features which we can combine in different configurations. The repairing agent may prioritise either the node cost or the travel distance. The focus of the agent may be local, with a greedy choice of next partition to re-connect, or global, maintaining a plan for all partitions. To handle the developing knowledge of the network conditions, the agent may revert to full replanning when a change is discovered, or try a minimal adjustment of the current plan in order to minimise the computation effort. For each configuration, we develop a number of heuristics for creating the plans. We evaluate the approach in simulation, varying the density of the connectivity graph and the level of damage suffered. We demonstrate that the plan repair method, while producing more expensive solutions, can require significantly less computation time, depending on the choice of heuristic. Finally, we evaluate the total time to repair the network for different speeds of agent, and we show the relative importance of the agent speeds on the two focuses. In particular, algorithms which prioritise mobility cost are preferred for slow agents, while faster moving agents should prioritise the radio node cost. [ABSTRACT FROM AUTHOR]

Target coverage is one of the major requirements in wireless sensor network applications, the purpose of which is to monitor a set of known targets in the region of interest. In this paper, we propose a localized self-deployment scheme, named as obstacle avoidance target involved deployment algorithm, for the deployment of randomly scattered mobile sensor nodes to cover predefined targets while maintaining connectivity with the base station in the presence of obstacles. We use the concept of potential field theory and relative neighborhood graph for the self-deployment of mobile sensor nodes in an unknown environment to achieve target coverage while preserving connectivity with the base station. Our proposed approach is localized in the sense that each decision taken by the sensor node is strictly based on information acquired from its neighboring sensors that are part of the relative neighborhood graph. The simulation results show the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Saving energy is a most important challenge in Mobile Wireless Sensor Networks (MWSNs) to extend the lifetime, and optimal coverage is the key to it. Therefore, this paper proposes a Centralized Immune-Voronoi deployment Algorithm (CIVA) to maximize the coverage based on both binary and probabilistic models. CIVA utilizes the multi-objective immune algorithm that uses the Voronoi diagram properties to provide a better trade-off between the coverage and the energy consumption. The CIVA algorithm consists from two phases to improve the lifetime and the coverage of MWSN. In the first phase, CIVA controls the positions and the sensing ranges of Mobile Sensor Nodes (MSNs) based on maximizing the coverage and minimizing the dissipated energy in mobility and sensing. While the second phase of CIVA adjusts the radio (sleep/active) of MSNs to minimize the number of active sensors based on minimizing the consumption energy in sensing and redundant coverage and preserving the coverage at high level. The performance of the CIVA is compared with the previous algorithms using Matlab simulation for different network configurations with and without obstacles. Simulation results show that the CIVA algorithm outperforms the previous algorithms in terms of the coverage and the dissipated energy for different networks configurations. [ABSTRACT FROM AUTHOR]

Visual sensor networks are receiving significant attention due to their potential applications ranging from surveillance to tracking domains. Nevertheless, due to the funneling effect, the unbalanced energy usage among visual sensor nodes (SNs) increases and leads to premature decrease in network lifetime. First, considering Rayleigh fading channel and routing models, we analyze the optimization of network lifetime by balancing the energy consumption among different SNs. From the analysis, it is revealed that the number of SNs and relay nodes and their locations have significant influence on limiting the energy hole problem and optimization of network lifetime. Based on the derived principle of energy balancing, we develop a heterogeneous SNs deployment strategy leading to optimization in network lifetime. Exhaustive simulation is performed, primarily to measure the extent of achieving our design goal of optimizing network lifetime while attaining energy balancing. We also measure the effect of placement errors on the performance and robustness of the scheme. The results show that even in the presence of placement error the performance is comparable and provides better robustness compared with the competing scheme. Further, the simulation results show that our scheme does not compromise with other performance metrics, e.g., end-to-end delay and throughput, while achieving the design goal. Finally, all the comparative results confirm our scheme’s supremacy over the competing schemes. [ABSTRACT FROM PUBLISHER]

The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. Aiming at the disadvantages of the existing node deployment and target localization scheme, this paper proposed a virtual forced algorithm as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the proposed algorithm attempted to maximize the sensor field coverage by judicious combination of attractive’ and used repulsive forces to determine virtual motion paths and the rate of movement for the randomly-placed sensors. At the same time, it also proposed a novel probabilistic target localization algorithm that was executed by the cluster head. It used the localization results by the cluster head to query only a few sensors (out of those that report the pre-sence of a target) for more detailed information. The simulation results show that the proposed scheme performs a one-time computation and determines sensor locations at the same time for all the sensor nodes. In addition, the proposed probabilistic localization algorithm can significantly reduce the energy consumption for target detection and location. [ABSTRACT FROM AUTHOR]

Existing node deployment algorithms for underwater sensor networks are nearly unable to improve the network coverage rate under the premise of ensuring the full network connectivity and do not optimize the communication and move energy consumption during the deployment. Hence, a node deployment algorithm based on connected dominating set (CDS) is proposed. After randomly sowing the nodes in 3D monitoring underwater space, disconnected nodes move to the sink node until the network achieves full connectivity. The sink node then performs centralized optimization to determine the CDS and adjusts the locations of dominated nodes. Simulation results show that the proposed algorithm can achieve a high coverage rate while ensuring full connectivity and decreases the communication and movement energy consumption during deployment. [ABSTRACT FROM AUTHOR]

Considering that deployment strategies for underwater sensor networks should contribute to fully connecting the networks, a Guaranteed Full Connectivity Node Deployment (GFCND) algorithm is proposed in this study. The GFCND algorithm attempts to deploy the coverage nodes according to the greedy iterative strategy, after which the connectivity nodes are used to improve network connectivity and fully connect the whole network. Furthermore, a Location Dispatch Based on Command Nodes (LDBCN) algorithm is proposed, which accomplishes the location adjustment of the common nodes with the help of the SINK node and the command nodes. The command nodes then dispatch the common nodes. Simulation results show that the GFCND algorithm achieves a comparatively large coverage percentage and a fully connected network; furthermore, the LDBCN algorithm helps the common nodes preserve more total energy when they reach their destination locations. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network can become partitioned due to node failure, requiring the deployment of additional relay nodes in order to restore network connectivity. This introduces an optimisation problem involving a tradeoff between the number of additional nodes that are required and the costs of moving through the sensor field for the purpose of node placement. This tradeoff is application-dependent, influenced for example by the relative urgency of network restoration. We propose a family of algorithms based on hierarchical objectives including complete algorithms and heuristics which integrate network design with path planning, recognising the impact of obstacles on mobility and communication. We conduct an empirical evaluation of the algorithms on random connectivity and mobility graphs, showing their relative performance in terms of node and path costs, and assessing their execution speeds. Finally, we examine how the relative importance of the two objectives influences the choice of algorithm. In summary, the algorithms which prioritise the node cost tend to find graphs with fewer nodes, while the algorithm which prioritise the cost of moving find slightly larger solutions but with cheaper mobility costs. The heuristic algorithms are close to the optimal algorithms in node cost, and higher in mobility costs. For fast moving agents, the node algorithms are preferred for total restoration time, and for slow agents, the path algorithms are preferred. [ABSTRACT FROM AUTHOR]

Node deployment is one of the most crucial issues in wireless sensor networks, and it is of realistic significance to complete the deployment task with multiple types of application requirements. In this paper, we propose a deployment strategy for multiple types of requirements to solve the problem of deterministic and grid-based deployment. This deployment strategy consists of three deployment algorithms, which are for different deployment objectives. First, instead of general random search, we put forward a deterministic search mechanism and the related cost-based deployment algorithm, in which nodes are assigned to different groups which are connected by near-shortest paths, and realize significant reduction of path length and deployment cost. Second, rather than ordinary nondirection deployment, we present a notion of counterflow and the related delay-based deployment algorithm, in which the profit of deployment cost and loss of transmission delay are evaluated, and achieve much diminishing of transmission path length and transmission delay. Third, instead of conventional uneven deployment based on the distances to the sink, we propose a concept of node load level and the related lifetime-based deployment algorithm, in which node distribution is determined by the actual load levels and extra nodes are deployed only where really necessary. This contributes to great improvement of network lifetime. Last, extensive simulations are used to test and verify the effectiveness and superiority of our findings. [ABSTRACT FROM PUBLISHER]