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**1 - 3**of**3**### Using Data Mules for Sensor Network Resiliency

, 2015

"... Abstract-In this paper, we study the problem of efficient data recovery using the data mules approach, where a set of mobile sensors with advanced mobility capabilities re-acquire lost data by visiting the neighbors of failed sensors, thereby improving network resiliency. Our approach involves defi ..."

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Abstract-In this paper, we study the problem of efficient data recovery using the data mules approach, where a set of mobile sensors with advanced mobility capabilities re-acquire lost data by visiting the neighbors of failed sensors, thereby improving network resiliency. Our approach involves defining the optimal communication graph and mules' placements such that the overall traveling time and distance is minimized regardless to which sensors crashed. We explore this problem under different practical network topologies such as general graphs, grids and random linear networks and provide approximation algorithms based on multiple combinatorial techniques. Simulation experiments demonstrate that our algorithms outperform various competitive solutions for different network models, and that they are applicable for practical scenarios. I. PROBLEM FORMULATION A data mule is a vehicle that physically carries a computer with storage between remote locations to effectively create a data communication link Let T be a data gathering tree rooted at root ρ spanning n wireless sensors positioned in the Euclidean plane, where data propagates from leaf nodes to ρ. We model the environment as a complete directed graph G = (V, E), where the node set represents the wireless sensors and the edge represents distance or time to travel between that sensors. We assume the sensors are deployed in rough geographic terrain with severe climatic conditions, which may cause sporadic failures of sensors. Clearly, if a sensor v fails, it is undesirable to lose the data it collected from its children in T , δ(v, T ). Thus, a group of data gathering robots must travel through δ(v, T ) and restore the lost information. We define this problem as (α, β)-Mule problem, where α is the number of simultaneous node failures and β is the number of traveling mules. the mule visits the children of v over the shortest tour, t(m, δ(v, T )), starting at node m ∈ V , where the length of the tour is equal to the Euclidean length of distances; the goal is to find a data gathering tree T , the placement of the mule m, and the shortest tours, t(m, δ(v, T )) for all v ∈ V , which minimize the total traveling distance given any sensor can fail. Formally, the objective is to have min T,m v∈V |t(m, δ(v, T ))|. In a similar way, we can define the problem for α > 1, β = 1 (see example for α = 2 in

### Article A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

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### Noname manuscript No. (will be inserted by the editor) Controlled Mobility in Stochastic and Dynamic Wireless Networks

"... Abstract We consider the use of controlled mobility in wireless networks where messages arriving randomly in time and space are collected by mobile receivers (collectors). The collectors are responsible for receiving these messages via wireless communication by dynamically adjusting their position i ..."

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Abstract We consider the use of controlled mobility in wireless networks where messages arriving randomly in time and space are collected by mobile receivers (collectors). The collectors are responsible for receiving these messages via wireless communication by dynamically adjusting their position in the network. Our goal is to utilize a combination of wireless transmission and controlled mobility to improve the throughput and delay performance in such networks. In the first part of the paper we consider a system with a single collector. We show that the necessary and sufficient stability condition for such a system is given by ρ < 1 where ρ is the average system load. We derive lower bounds for the average message waiting time in the system and develop policies that are stable for all loads ρ < 1 and have asymptotically optimal delay scaling. We show that the combination of mobility and) with the system load ρ in contrast to the 1 Θ( (1−ρ) 2) delay scaling in the corresponding system where the collector visits each message location. In the second part of the paper we consider the system with multiple collectors. In the case where simultaneous transmissions to different collectors do not interfere with each other, we show that the stability condition is given by ρ < 1, where ρ is the system load on multiple collectors. We develop lower bounds on delay and generalize policies established for the single collector case to multiple wireless transmission results in a delay scaling of Θ ( 1