TY - JOUR
T1 - Distributed Learning Algorithms for Optimal Data Routing in IoT Networks
AU - Rossi, Michele
AU - Centenaro, Marco
AU - Ba, Aly
AU - Elleuch, Salma
AU - Erseghe, Tomaso
AU - Zorzi, Michele
PY - 2020/2
Y1 - 2020/2
N2 - We consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a source-specific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multi-hop fashion until it reaches a data collector (the IoT gateway). The problem we address is to compute a suitable rate-distortion working point for the compression scheme at the source nodes, while jointly assessing the most energy efficient routing paths for the data they transmit, under channel access, distortion and capacity constraints. This is formulated as a multi-objective optimization problem that is solved through distributed learning algorithms, where source coding and routing configurations emerge as the result of local interactions among the network devices. Our final algorithm is based on the alternating direction method of multipliers (ADMM), which is accelerated using the most recent findings from the literature. As a result, it has faster convergence (up to three times) to the global optimum than standard ADMM. Numerical results are discussed for selected network scenarios, emphasizing the interrelations that exist between signal recon- struction quality at the IoT gateway and total transport energy in the network.
AB - We consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a source-specific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multi-hop fashion until it reaches a data collector (the IoT gateway). The problem we address is to compute a suitable rate-distortion working point for the compression scheme at the source nodes, while jointly assessing the most energy efficient routing paths for the data they transmit, under channel access, distortion and capacity constraints. This is formulated as a multi-objective optimization problem that is solved through distributed learning algorithms, where source coding and routing configurations emerge as the result of local interactions among the network devices. Our final algorithm is based on the alternating direction method of multipliers (ADMM), which is accelerated using the most recent findings from the literature. As a result, it has faster convergence (up to three times) to the global optimum than standard ADMM. Numerical results are discussed for selected network scenarios, emphasizing the interrelations that exist between signal recon- struction quality at the IoT gateway and total transport energy in the network.
KW - Flow allocation
KW - Internet of Things
KW - alternating direction method of multipliers (ADMM)
KW - compression
KW - optimization
KW - source coding
UR - http://www.scopus.com/inward/record.url?scp=85079908850&partnerID=8YFLogxK
U2 - 10.1109/TSIPN.2020.2975369
DO - 10.1109/TSIPN.2020.2975369
M3 - Journal article
SN - 2373-776X
VL - 6
SP - 179
EP - 195
JO - IEEE Transactions on Signal and Information Processing over Networks
JF - IEEE Transactions on Signal and Information Processing over Networks
M1 - 9006950
ER -