Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes

Proximity-driven social interactions and their impact on the throughput scaling of wireless networks

We present an analytical framework to investigate the interplay between a communication graph and an overlay of social  relationships.  We focus on geographical distance as the key element that interrelates the concept of routing in a communication network with the dynamics of interpersonal relations on the corresponding social graph. We identify  classes of social relationships that let the ensuing system  scale---i.e., accommodate a large number of users given only finite amount of resources. We establish that geographically concentrated communication patterns are indispensable to network scalability. We  further examine the impact of such proximity-driven interaction patterns on the throughput scaling of wireless networks, and show that, when social communications are geographically localized, the maximum per-node throughput scales approximately as $1/\log n$, which is significantly better than the well-known bound of $1/\sqrt{n \log n}$ for the uniform communication model

Load Balancing Hashing for Geographic Hash Tables

In this paper, we address the problem of balancing the network traffic load generated when querying a geographic hash table. State-of-the-art approaches can be used to improve load balancing by changing the underlying geo-routing protocol used to forward queries in the geographic hash table. However, this comes at the expense of considerably complicating the routing process, which no longer occurs along (near) straightline trajectories, but requires computing complex geometric transformations. Thus, current load balancing approaches are impractical in application scenarios where the nodes composing the geographic hash table have limited computational power, such as in most wireless sensor networks. In this paper, we propose a novel approach to solve the traffic load balancing problem in geographic hash tables: instead of changing the (near) straight-line geo-routing protocol used to send a query from the node issuing the query (the source) to the node managing the queried key (the destination), we propose to "reverse engineer" the hash function so that the resulting destination density, when combined with a given source density, yields a perfectly balanced load distribution. We first formally characterize the desired destination density as a solution of a complex integral equation. We then present explicit destination density functions (taken from the family of Beta distributions) yielding quasi-perfect load balancing under the assumption of uniformly distributed sources. Our theoretical results are derived under an infinite node density model. In order to prove practicality of our approach, we have performed extensive simulations resembling realistic wireless sensor network deployments showing the effectiveness of our approach in considerably improving load balancing. Differently from previous work, the load balancing technique proposed in this paper can be readily applied in geographic hash tables composed of computationally constrained nodes, as it is typically the case in wireless sensor networks

Throughput optimization for data collection in wireless sensor networks

Wireless sensor networks are widely used in many application domains in recent years. Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this dissertation, we aim to study the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Various communication scenarios are considered in our analysis, such as with a single sink or multiple sinks, randomly-deployed or arbitrarily- deployed sensors, and different communication models. For both randomly-deployed and arbitrarily-deployed sensor networks, an efficient collection algorithm has been proposed under protocol interference model and physical interference model respec- tively. We can prove that its performance is within a constant factor of the optimal for both single sink and regularly-deployed multiple sinks cases. We also study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss per- formance implications. In addition, we further discuss the problem of data collection capacity under Gaussian channel model

Hierarchical Routing over Dynamic Wireless Networks

Wireless network topologies change over time and maintaining routes requires frequent updates. Updates are costly in terms of consuming throughput available for data transmission, which is precious in wireless networks. In this paper, we ask whether there exist low-overhead schemes that produce low-stretch routes. This is studied by using the underlying geometric properties of the connectivity graph in wireless networks.Comment: 29 pages, 19 figures, a shorter version was published in the proceedings of the 2008 ACM Sigmetrics conferenc