On the truck dispatching problem

We present an exact partial enumerative algorithm for the maximum clique problem. The pruning device used is derived from graph colorings. Pruning of the search tree is accomplished not only by the number of colors used to color a tree subproblem but also by using information gained in the process of coloring. This leads to increased pruning which translates into improved computational performance. Experimental results on test problems are presented.

An exact algorithm for the maximum clique problem with accelerated pruning

http://www.citsm.umd.edu/documents/final-reports/MillerHooks%20Zhang%20Faturechi%202011%20Final%20Report.pdf

Measuring and maximizing resilience of freight transportation networks

We investigate the following fundamental question-how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

Fast Data Collection in Tree-Based Wireless Sensor Networks

Disaster relief presents many unique logistics challenges, with problems including damaged trans- portation infrastructure, limited communication, and coordination of multiple agents. Central to disaster relief logistics is the distribution of life-saving commodities to beneficiaries. Operations research models have potential to help relief agencies save lives and money, maintain standards of humanitarianism and fairness and maximize the use of limited resources amid post-disaster chaos. Through interviews with aid organizations, reviews of their publications, and a literature review of operations research models in transportation of relief goods, this paper provides an analysis of the use of such models from the perspective of both practitioners and academics. With the complexity of disaster relief distribution and the relatively small number of journal articles written on it, this is an area with potential for helping relief organizations and for tremendous growth in operations research.

/pdf/disaster-relief-routing-integrating-research-and-practice-51sv6aijxa.pdf

Disaster relief routing: Integrating research and practice

Wireless sensor networks promise a new paradigm for gathering data via collaboration among sensors spreading over a large geometrical region. Many real-time applications impose stringent delay requirements and ask for time-efficient schedules of data aggregations in which sensed data at sensors are combined at intermediate sensors along the way towards the data sink. The Minimum Data Aggregation Time (MDAT) problem is to find the schedule that routes data appropriately and has the shortest time for all requested data to be aggregated to the data sink.

In this paper we study the MDAT problem with uniform transmission range of all sensors. We assume that, in each time round, data sent by a sensor reaches exactly all sensors within its transmission range, and a sensor receives data if it is the only data that reaches the sensor in this time round. We first prove that this problem is NP-hard even when all sensors are deployed a grid and data on all sensors are required to be aggregated to the data sink. We then design a (Δ–1)-approximation algorithm for MDAT problem, where Δ equals the maximum number of sensors within the transmission range of any sensor. We also simulate the proposed algorithm and compare it with the existing algorithm. The obtained results show that our algorithm has much better performance in practice than the theoretically proved guarantee and outperforms other algorithm.

Minimum data aggregation time problem in wireless sensor networks

Crowd psychology plays an important role in determining the kind of activities that a person performs. In reality, in a social network, crowd influence has been observed and it cannot be ignored when considering information diffusion problems. In this paper, we model crowd influence as a hyperedge $e=(H_e,v)$ with weight $0\leq P_e\leq 1$ , where $H_e$ is the head node set and $v$ is the tail node, means $v$ will be activated by $H_e$ with probability $P_e$ only after each node in $H_e$ is activated. Then, the Social Influence Maximization Problem in Hypergraph (SIMPH) aims to select $k$ initially-influenced seed users in a directed hypergraph $G=(V,E,P)$ . The objective is to maximize the expected number of eventually-influenced users. We show that SIMPH is NP-hard and the objective function is neither submodular nor supermodular. We develop a lower bound and an upper bound that are submodular. We prove that maximizing these two bounds are still NP-hard under IC model. Then, we present a D-SSA algorithm for general weighted social influence maximization problem preserving $(1-1/e-\epsilon)$ -approximation. We formulate a sandwich approximation framework, which preserves a theoretical analysis result. Finally, we evaluate our algorithm on real world data sets. The results show the effectiveness and the efficiency of the proposed algorithm.

Social Influence Maximization in Hypergraph in Social Networks

Wireless sensor networks promise a new paradigm for gathering data via collaboration among sensors spreading over a large geometrical region. Many applications impose delay requirements for data gathering and ask for time-efficient schedules for aggregating sensed data and sending to the data sink. In this paper, the authors study the minimum data aggregation time problem under collision-free transmission model. In each time round, data sent by a sensor reaches all sensors within its transmission range, but a sensor can receive data only when it is the only data that reaches the sensor. The goal is to find the method that schedules data transmission and aggregation at sensors so that the time for all requested data to be sent to the data sink is minimal. The authors propose a new approximation algorithm for this NP-hard problem with guaranteed performance ratio $$\frac{{7\Delta }}{{\log _{2} {\left| {\text{S}} \right|}}} + c$$
 , which significantly reduces the current best ratio of Δ 1, where S is the set of sensors containing source data, Δ is the maximal number of sensors within the transmission range of any sensor, and c is a constant. The authors also conduct extensive simulation, the obtained results justify the improvement of proposed algorithm over the existing one.

Improved Algorithm for Minimum Data Aggregation Time Problem in Wireless Sensor Networks

Group plays an important role in social society. Much of the world’s decision or work is done by groups and teams. A group’s decision should be made based on most of the members in the group that reach agreement on a concerned topic. If we want to spread a topic and maximize the total number of activated groups in a social network, which seed users should we choose. In this article, we will study a new influence maximization (IM) problem which focuses on the number of groups activated by some concerned topic or information. A group is said to be activated if $\beta $ percent of users in this group are activated. Group IM (GIM) aims to select $k$ seed users such that the number of eventually activated groups is maximized. We first analyze the complexity and approximability of GIM, which is NP-hard, and the objective function presented in this article is proven to be neither submodular nor supermodular. We develop an upper bound problem and a lower bound problem whose objective functions are submodular. Then, an algorithm based on group coverage will be proposed, and the Sandwich framework is formulated with theoretical analysis to solve GIM. Our experiments verify the effectiveness of our method, as well as the advantage of our method against the other heuristic methods.

Group Influence Maximization Problem in Social Networks

In recent years, online social media has flourished, and a large amount of information has spread through social platforms, changing the way in which people access information. The authenticity of information content is weakened, and all kinds of misinformation rely on social media to spread rapidly. Network space governance and providing a trusted network environment are of critical significance. In this article, we study a novel problem called activity minimization of misinformation influence (AMMI) problem that blocks a node set from the network such that the total amount of misinformation interaction between nodes (TAMIN) is minimized. That is to say, the AMMI problem is to select $K$ nodes from a given social network $G$ to block so that the TAMIN is the smallest. We prove that the objective function is neither submodular nor supermodular and propose a heuristic greedy algorithm (HGA) to select top $K$ nodes for removal. Furthermore, in order to evaluate our proposed method, extensive experiments have been carried out on three real-world networks. The experimental results demonstrate that our proposed method outperforms comparison approaches.

Jianming Zhu

Papers

Minimum data aggregation time problem in wireless sensor networks

Social Influence Maximization in Hypergraph in Social Networks

Improved Algorithm for Minimum Data Aggregation Time Problem in Wireless Sensor Networks

Group Influence Maximization Problem in Social Networks

Activity Minimization of Misinformation Influence in Online Social Networks