Distributed Maximum Likelihood Sensor Network Localization

We propose a class of convex relaxations to solve the sensor network localization problem, based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the relaxations, depends on the noise probability density function (PDF) of the collected measurements. We derive a computational efficient edge-based version of this ML convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge-based convex programs locally by communicating only with their close neighbors. This algorithm relies on the alternating direction method of multipliers (ADMM), it converges to the centralized solution, it can run asynchronously, and it is computation error-resilient. Finally, we compare our proposed distributed scheme with other available methods, both analytically and numerically, and we argue the added value of ADMM, especially for large-scale networks

Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization

Dual decomposition has been successfully employed in a variety of distributed convex optimization problems solved by a network of computing and communicating nodes. Often, when the cost function is separable but the constraints are coupled, the dual decomposition scheme involves local parallel subgradient calculations and a global subgradient update performed by a master node. In this paper, we propose a consensus-based dual decomposition to remove the need for such a master node and still enable the computing nodes to generate an approximate dual solution for the underlying convex optimization problem. In addition, we provide a primal recovery mechanism to allow the nodes to have access to approximate near-optimal primal solutions. Our scheme is based on a constant stepsize choice and the dual and primal objective convergence are achieved up to a bounded error floor dependent on the stepsize and on the number of consensus steps among the nodes

The introduction of mandatory inter-municipal cooperation in small municipalities: preliminary lessons from Italy

PurposeThis article studies effects of mandatory inter-municipal cooperation (IMC) in small Italian municipalities. Data from 280 small Italian municipalities on effects of IMC in terms of higher efficiency, better effectiveness of local public services, and greater institutional legitimacy of the small municipalities participating in IMC have been investigated against four variables: size; geographical area; type of inter-municipal integration and IMC membership (the presence in the IMC of a bigger municipality, the so-called big brother).Design/methodology/approachData were gathered from a mail survey that was sent to a random sample of 1,360 chief financial officers acting in municipalities of under 5,000 inhabitants, stratified by size (0–1,000 and 1,001–5,000) and geographic area (North, Center, and South) criteria. To analyze dependency relationships between the three potential effects of participating in IMC and possible explanatory variables, we used a logistic regression model as the benefits were binarily categorized (presence or absence of benefits).FindingsFindings show that in more than two-thirds of the municipalities participating in IMC there were benefits in terms of costs reduction and better public services, whereas greater institutional legitimacy was detected in about half of the cases. Our statistical analysis with logistic regression highlighted that IMC type is particularly critical for explaining successful IMC. In particular, positive effects of IMC were mainly detected in those small municipalities that promoted a service delivery organization rather than participating in service delivery agreements or opting for mixed arrangements of joint public services delivery.Originality/valueThe paper focuses on small municipalities where studies are usually scant. Our analysis highlighted that the organizational setting is particularly critical for explaining successful IMC