Haiming Jin

Data Acquisition for Real-Time Decision-Making under Freshness Constraints

The paper describes a novel algorithm for timely sensor data retrieval in resource-poor environments under freshness constraints. Consider a civil unrest, national security, or disaster management scenario, where a dynamic situation evolves and a decision-maker must decide on a course of action in view of latest data. Since the situation changes, so is the best course of action. The scenario offers two interesting constraints. First, one should be able to successfully compute the course of action within some appropriate time window, which we call the decision deadline. Second, at the time the course of action is computed, the data it is based on must be fresh (i.e., within some corresponding validity interval). We call it the freshness constraint. These constraints create an interesting novel problem of timely data retrieval. We address this problem in resource-scarce environments, where network resource limitations require that data objects (e.g., pictures and other sensor measurements pertinent to the decision) generally remain at the sources. Hence, one must decide on (i) which objects to retrieve and (ii) in what order, such that the cost of deciding on a valid course of action is minimized while meeting data freshness and decision deadline constraints. Such an algorithm is reported in this paper. The algorithm is shown in simulation to reduce the cost of data retrieval compared to a host of baselines that consider time or resource constraints. It is applied in the context of minimizing cost of finding unobstructed routes between specified locations in a disaster zone by retrieving data on the health of individual route segments.

Secure data collection in constrained tree-based Smart Grid environments

To facilitate more efficient control, massive amounts of sensors or measurement devices will be deployed in the Smart Grid. Data collection then becomes non-trivial. In this paper, we study the scenario where a data collector is responsible for collecting data from multiple measurement devices, but only some of them can communicate with the data collector directly. Others have to rely on other devices to relay the data. We first develop a communication protocol so that the data reported by each device is protected again honest-but-curious data collector and devices. To reduce the time to collect data from all devices within a certain security level, we formulate our approach as an integer linear programming problem. As the problem is NP-hard, obtaining the optimal solution in a large network is not very feasible. We thus develop an approximation algorithm to solve the problem. We test the performance of our algorithm using real topologies. The results show that our algorithm successfully identifies good solutions within reasonable amount of time.

Non-Cooperative Game Based Social Welfare Maximizing Bandwidth Allocation in WSNs

In this paper, we deal with possible data transmission congestion on the sink node in wireless sensor networks (WSNs). We consider a scenario in which all the sensor nodes have a certain amount of storage space and acquire data from the surroundings at heterogeneous speed. Because receiving bandwidth of the sink node is limited, a proper bandwidth allocation mechanism should be implemented to avoid possible congestion or data loss due to the overflow of some sensor nodes. To address this problem, we firstly design a novel bandwidth allocation mechanism, SWM, that can maximize the social utility, an indicator of every sensor nodes satisfaction degree and the social fairness. Furthermore, we model the allocation process under the SWM as a noncooperative game and figure out the unique Nash Equilibrium. The uniqueness of the equilibrium demonstrates that this network will actually approach to a fair and stable state.

SKEPRID: Pose and Illumination Change-Resistant Skeleton-Based Person Re-Identification

Currently, the surveillance camera-based person re-identification is still challenging because of diverse factors such as people’s changing poses and various illumination. The various poses make it hard to conduct feature matching across images, and the illumination changes make color-based features unreliable. In this article, we present SKEPRID,1 a skeleton-based person re-identification method that handles strong pose and illumination changes jointly. To reduce the impacts of pose changes on re-identification, we estimate the joints’ positions of a person based on the deep learning technique and thus make it possible to extract features on specific body parts with high accuracy. Based on the skeleton information, we design a set of local color comparison-based cloth-type features, which are resistant to various lighting conditions. Moreover, to better evaluate SKEPRID, we build the PO8LI2 dataset, which has large pose and illumination diversity. Our experimental results show that SKEPRID outperforms state-of-the-art approaches in the case of strong pose and illumination variation.