Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Abstract

Abstract-Minimum-Latency Aggregation Scheduling (MLAS)is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for globalscale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log 3 (n)) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [13]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.

Leveraging multiple channels in ad hoc networks

“…Data Aggregation. In single-channel networks, there is a long line of research on data aggregation under different settings in the protocol model [30,31,32] and the SINR model [1,2,10,14,16,17,23,24]. Regarding distributed solutions in the SINR model, a distributed aggregation algorithm with uniform power assignment was proposed in [24], which achieves a latency upper bound of O(D + ∆).…”

“…Regarding distributed solutions in the SINR model, a distributed aggregation algorithm with uniform power assignment was proposed in [24], which achieves a latency upper bound of O(D + ∆). Assuming a model where every node in the network knows its position, the network diameter and the number of neighbors, Li et al [23] presented a distributed algorithm with a latency bound of O(K), where K is the logarithm of the ratio between the length of the longest link and that of the shortest link. This result additionally needs that nodes can adjust the transmission power arbitrarily.…”

Data Inconsistency Evaluation for Cyberphysical System

“…, ← ⌀; (4) for all such that 1 ≤ ≤ do (5) if | | ≤ | | then (6) put into ; (7) else (8) put into ; (9) ← ∪ ⟨ , ⟩; (10) return ; Algorithm 3: ℎ (Δ).…”

Maximum Achievable Throughput in a Wireless Sensor Network Using In-Network Computation for Statistical Functions

“…Constraint (19) ensures that only one Iset is chosen in slot k and constraint (20) allows a link to be active in slot k if and only if an Iset containing it is active in that slot. Note that all the links in any given Iset satisfy the interference constraints by construction and hence the original interference constraints (15)(16) are redundant in the problem formulation.…”

“…The discrete time dimension disappears from the constraints (7)(8)(9)(10)(11)(12)(13)(14) and (19)(20) when we sum each of them over k and divide with T m , leading to problem P 2 (m). Note that after this summation, all q s,w,p i (k) variables cancel from the equations (8), (9) and (10), except when k = 0 and k = T m .…”