Amorphous Placement and Informed Diffusion for Timely Field Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Abstract

Abstract-Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance.

ESC: Energy Synchronized Communication in sustainable sensor networks

“…However, before node p is updated, it would continue to deliver packets according to the old (obsolete) working schedule of node b, which could suffer significant greater packet loss than necessary. For example, as shown in Figure 11, node b reduces its duty-cycle by changing its working schedule from (3,6,9) to (6,9). Assuming predecessor node p has a packet ready at time 1, unaware of the new working schedule at node b, node p would try to deliver the packet with active instance sequence (3, 6, 9, 3, 6, 9, ...) until the packet is successfully delivered or the number of retransmissions reaches the bound R max .…”

“…On the other hand, an adjustment would add the augmented active instance to the previous schedule. Consequently, the new working schedule produced by the adjustment could be (3,6,8), where (3,6) are identical to the previous working schedule.…”

“…With the increasing need for cyber-physical interaction, Wireless Sensor Networks (WSN) have emerged as a key technology for many long-term applications, such as military surveillance [1], [2], field monitoring [3] and assisted living [4]. Due to the stringent constraints on cost and form factors, traditional battery-powered sensor networks must balance the tradeoff between sustainability and system performance [5]- [7].…”

Data De-Duplication Aware Clustering for Optimized Storage in Wireless Multimedia Sensor Networks

“…In this work, a dynamic-programming algorithm is used to determine an optimal execution order of queries. Examples of other studies that proposed coordination strategies include techniques that ensure efficient storage, caching, and exchange of spatio-temporal data [6,7] or aggregates thereof [8]. In our work, we focus on reducing overheads by adding Data De-duplication functionality to a subset of nodes at various hierarchies, without concern to resource or energy constraints.…”

The filter-placement problem and its application to minimizing information multiplicity

“…In that work, a dynamic-programming algorithm is used to determine an optimal execution order of queries. Examples of other studies that proposed coordination strategies include techniques that ensure efficient storage, caching, and exchange of spatio-temporal data [22,23] or aggregates thereof [21].…”