Designing <Emphasis Type="Italic">k</Emphasis>-coverage schedules in wireless sensor networks

Some sensor network applications require k-coverage to ensure the quality of surveillance. Meanwhile, energy is another primary concern for sensor networks. In this paper, we investigate the Sensor Scheduling for k-Coverage (SSC) problem which requires to efficiently schedule the sensors, such that the monitored area can be k-covered throughout the whole network lifetime with the purpose of maximizing network lifetime. The SSC problem is NP-hard and we propose two heuristic algorithms under different scenarios. In addition, we develop a guideline for users to better design a sensor deployment plan to save energy by employing a density control scheme. Simulation results are presented to evaluate our proposed algorithms.     

Strong minimum energy hierarchical topology in wireless sensor networks

Given a set of \(n\) sensors, the strong minimum energy topology (SMET) problem in a wireless sensor network is to assign transmit powers to all sensors such that (i) the graph induced only using the bi-directional links is connected, that is, there is a path between every pair of sensors, and (ii) the sum of the transmit powers of all the sensors is minimum. This problem is known to be NP-hard. In this paper, we study a special case of the SMET problem, namely , the \(k\)-strong minimum energy hierarchical topology (\(k\)-SMEHT) problem. Given a set of \(n\) sensors and an integer \(k\), the \(k\)-SMEHT problem is to assign transmission powers to all sensors such that (i) the graph induced using only bi-directional links is connected, (ii) at most \(k\) nodes of the graph induced using only bi-directional links have two or more neighbors, that is they are non-pendant nodes, and (iii) the sum of the transmit powers of all the sensors in \(G\) is minimum. We show that \(k\)-SMEHT problem is NP-hard for arbitrary \(k\). However, we propose a \(\frac{k+1}{2}\)-approximation algorithm for \(k\)-SMEHT problem, when \(k\) is a fixed constant. Finally, we propose a polynomial time algorithm for the \(k\)-SMEHT problem for \(k=2\).     

Minimum power assignment in wireless ad hoc networks with spanner property

Power assignment for wireless ad hoc networks is to assign a power for each wireless node such that the induced communication graph has some required properties. Recently research efforts have focused on finding the minimum power assignment to guarantee the connectivity or fault-tolerance of the network. In this paper, we study a new problem of finding the power assignment such that the induced communication graph is a spanner for the original communication graph when all nodes have the maximum power. Here, a spanner means that the length of the shortest path in the induced communication graph is at most a constant times of the length of the shortest path in the original communication graph. Polynomial time algorithm is given to minimize the maximum assigned power with spanner property. The algorithm also works for any other property that can be tested in polynomial time and is monotone. We then give a polynomial time approximation method to minimize the total transmission radius of all nodes. Finally, we propose two heuristics and conduct extensive simulations to study their performance when we aim to minimize the total assigned power of all nodes. The author is partially supported by NSF CCR-0311174.     

OMEGa: an optimistic most energy gain method for minimum energy multicasting in wireless ad hoc networks

In wireless ad hoc networks where every device runs on its own battery, the energy consumption is critical to lengthen the network lifetime. The communication among devices in the network can be categorized as unicasting and multicasting (including broadcasting). For the case of unicasting, computing the energy optimal path between the two communicating nodes is polynomially solvable by computing the shortest path. But for the case of multicasting, shortest path or minimum spanning tree does not guarantee an energy optimal communication. In this paper, we present our novel approach, Optimistic Most Energy Gain (OMEGa) method, for the minimum energy multicasting in wireless ad hoc networks. OMEGa aims at maximum utilization of Wireless Multicast Advantage (WMA), which essentially means covering more nodes by using larger energy. Both theoretical and experimental analysis shows OMEGa method performs very well. Research is partially supported by NSF and Air Force grants.     

A modified hierarchical production planning system integrated with MRP: A case study

In this paper, a hierarchical planning system is proposed which integrates aggregate capacity planning with MRP. This system is to be implemented in a metal box manufacturing company which multi-user MRP system covering manufacturing activities as well as procurement sales order processing and accounting systems. The hierarchical planning system includes a medium-range aggregate planning model adapted to the firm's requirements and strategies. The model consists of a mathematical formulation which covers labour capacity has already installed a constraints and includes certain cost estimations in the objective function. The planning horizon of the medium range planning is taken as twelve months in order to cover sales seasonality. The aggregate production quantities resulting from the optimized medium-range planning model are disaggregated according to procedures already found in the literature. Furthermore, the theoretical infeasibilities pertaining to the disaggregation procedures are also resolved in an heuristic manner. Using the latter modified disaggregation procedure, a feasible disaggregated plan is generated for the whole planning horizon. The proposed plan is compared with the current production policy of the firm and it is observed that the proposed plan leads to backorder reduction.     

On optimal placement of relay nodes for reliable connectivity in wireless sensor networks

The paper addresses the relay node placement problem in two-tiered wireless sensor networks. Given a set of sensor nodes in Euclidean plane, our objective is to place minimum number of relay nodes to forward data packets from sensor nodes to the sink, such that: 1) the network is connected, 2) the network is 2-connected. For case one, we propose a (6+ε)-approximation algorithm for any ε > 0 with polynomial running time when ε is fixed. For case two, we propose two approximation algorithms with (24+ε) and (6/T+12+ε), respectively, where T is the ratio of the number of relay nodes placed in case one to the number of sensors. We further extend the results to the cases where communication radiuses of sensor nodes and relay nodes are different from each other.     

An access network design problem with end-to-end QoS constraints

In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinear mixed integer programming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure.     

A systematic approach for supply chain conflict detection with a hierarchical Petri Net extension

Coordinating and managing distributed entities in a supply chain is a challenging task due, in part, to conflicts present in such systems. If not handled effectively, the conflict can degrade the performance of the system as a whole due to the fact that each individual entity may be working towards goals that sub-optimize the integrated system. Therefore, the ability to discover conflicts would be a valuable asset, particularly if the discovery occurred proactively. This paper presents a methodology, extending the concept of basic Petri Nets, to discover supply chain conflict before they occur and cause detrimental effects to system performance. The approach involves linking hierarchical levels of the supply chain system and detecting conflicts occurring when the single entities, each optimized for it own operations, are combined together in a supply chain. These conflicts are not obvious or intuitive in examining the single entities of the supply chain, but when integrated the conflicts are discovered by the methodology. We applied the proposed methodology on a real-world supply chain to illustrate the validity of the tool. Although, further research is needed to fully explore this method of conflict detection, we believe that this research does indeed provide some much needed insight into the daunting task of conflict discovery and therefore proactive handling of these potentially negative occurrences in the supply chain.     

Efficient point coverage in wireless sensor networks

We study minimum-cost sensor placement on a bounded 3D sensing field, R, which comprises a number of discrete points that may or may not be grid points. Suppose we have ℓ types of sensors available with different sensing ranges and different costs. We want to find, given an integer σ ≥ 1, a selection of sensors and a subset of points to place these sensors such that every point in R is covered by at least σ sensors and the total cost of the sensors is minimum. This problem is known to be NP-hard. Let k_i denote the maximum number of points that can be covered by a sensor of the ith type. We present in this paper a polynomial-time approximation algorithm for this problem with a proven approximation ratio . In applications where the distance of any two points has a fixed positive lower bound, each k_i is a constant, and so we have a polynomial-time approximation algorithms with a constant guarantee. While γ may be large, we note that it is only a worst-case upper bound. In practice the actual approximation ratio is small, even on randomly generated points that do not have a fixed positive minimum distance between them. We provide a number of numerical results for comparing approximation solutions and optimal solutions, and show that the actual approximation ratios in these examples are all less than 3, even though γ is substantially larger. This research was supported in part by NSF under grant CCF-04080261 and by NSF of China under grant 60273062.     

Relay node placement in two-tiered wireless sensor networks with base stations

This paper addresses the relay node placement problem in two-tiered wireless sensor networks with base stations, which aims to deploy a minimum number of relay nodes to achieve certain coverage and connectivity requirement. Under the assumption that the communication range of the sensor nodes is no more than that of the relay node, we present a polynomial time (5+?)-approximation algorithm for the 1-coverage 1-connected problem. Furthermore, we consider the fault tolerant problem in the network, we present a polynomial time (20+?)-approximation algorithm for the 2-coverage 2-connected problem, where ? is any given positive constant. For the k-coverage 2-connected situation, we present a polynomial time (15k?10+?)-approximation algorithm.     

Total energy optimal multicasting in wireless ad hoc networks

In this paper, we present our novel algorithm, SOR (Shrinking Overlapped Range), for the minimum energy multicasting in wireless ad hoc networks. The heuristics in the literature have not considered changing the intermediate tree structure and this may result in worse performance even after local improvements at the end. In SOR, we extensively change the intermediate tree structure to maintain tighter structure in terms of energy consumption. We do so by shrinking the overlapped transmission range following the idea of WMA (wireless multicast advantage) property and by allowing the selection of internal transmissions which further changes the tree structure. Both theoretical analysis and experimental results show SOR outperforms other heuristics in the literature. Research is partially supported by NSF and Air Force grants.     

Correct accounting for duty drawbacks with outward and inward processing in global production networks

We develop a new model for the correct accounting of customs duties levied on a product. We examine inward and outward processing – that is, processed components can be either imported or produced in a foreign country – in the strategic planning of a global production network. This complex modeling problem is structured with path variables, and the duty drawbacks can be simultaneously and correctly entered for n production stages in m market regions (with corresponding duty regions) for all products with a maximum n-level bill of materials. We present a case study from the automotive industry to examine whether or not the possibility of future duty rate changes or free trade agreements, such as one between the United States and the European Union, could affect the design of a production network and hence should be considered in strategic planning. We show that correctly accounting for duty drawbacks can lead to changes in the global footprint of production. We also demonstrate that intercontinental trade barriers (in the form of duties) diminish working capital and entail longer delivery routes. Eliminating these political trade barriers could increase the returns to capital while reducing both delivery lead times and environmental costs.     

Coverage by directional sensors in randomly deployed wireless sensor networks

We study a novel “coverage by directional sensors” problem with tunable orientations on a set of discrete targets. We propose a Maximum Coverage with Minimum Sensors (MCMS) problem in which coverage in terms of the number of targets to be covered is maximized whereas the number of sensors to be activated is minimized. We present its exact Integer Linear Programming (ILP) formulation and an approximate (but computationally efficient) centralized greedy algorithm (CGA) solution. These centralized solutions are used as baselines for comparison. Then we provide a distributed greedy algorithm (DGA) solution. By incorporating a measure of the sensors residual energy into DGA, we further develop a Sensing Neighborhood Cooperative Sleeping (SNCS) protocol which performs adaptive scheduling on a larger time scale. Finally, we evaluate the properties of the proposed solutions and protocols in terms of providing coverage and maximizing network lifetime through extensive simulations. Moreover, for the case of circular coverage, we compare against the best known existing coverage algorithm.     

Traffic engineering of tunnel-based networks with class specific diversity requirements

Tunnel-based networks such as Multi-protocol Label switching (MPLS) are suitable for providing diversity guarantees to different service classes or customers. Based on the number of active tunnels to handle, router capabilities can be taxed due to the limited amount of memory and/or processing power of these routers. In this paper, we present a mixed-integer linear program formulation for a traffic engineering problem where such tunnel restrictions are taken into account in addition to standard capacity constraints while addressing diversity requirement of services. Due to large size of the formulation, we also present an accompanied solution approach based on Lagrangian relaxation and sub-gradient optimization. We then present results towards impact of diversity constraint upon the tunneling and capacity restrictions. We observed that the networks having higher amounts of capacity and demands with higher level of survivability are much more sensitive to number of allowed tunnels in the network. The impact is even more prominent for sparsely-connected, large-sized networks.     

大数据中的管理问题:基于大数据的资源观

作为重要的战略资源,大数据中包含诸多关键的管理问题.文章首先评述了基于不同视角对大数据的认识.然后,从管理的视角看大数据,指出大数据是一类重要的战略性信息资源,并从复杂性、决策有用性、高速增长性、价值稀疏性、可重复开采性和功能多样性等6个方面探究了大数据资源的管理特征.最后,提炼并探讨了大数据资源的获取问题、加工问题、应用问题、产权问题、产业问题和法规问题等6个方面的关键管理问题.     

Governance dynamics in inter-organizational networks: A meta-ethnographic study

Governance has been a pervasive theme in the discussions of strategy and management in the context of inter-organizational networks. To date, network researchers call for a dynamic theory of governance to deal with increased uncertainty in the network environment. In response to this call, this study aims to generate a better understanding of how and why governance in inter-organizational networks can change over time and what implications these governance dynamics have for the network and its members. A meta-ethnographic analysis of 15 longitudinal case studies reveals that network actors can combine relational and formal governance in different ways over time, thereby constituting three governance patterns: the relational celebration pattern, the competitive protection pattern, and the forced facilitation pattern. The relational celebration and competitive protection patterns can generate favorable outcomes for the network and its members if disagreement about the formal governance terms is eliminated by revising these terms and/or repeating the pattern with a subset of the network members. In the case of the forced facilitation pattern, favorable network outcomes emerge if different types of relational governance are balanced. By detailing how and why governance patterns emerge and can generate favorable outcomes for the network and its members, the present research creates a better understanding of governance dynamics for researchers, business practitioners, and policymakers.     

Improved adaptive group testing algorithms with applications to multiple access channels and dead sensor diagnosis

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary—the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants. A preliminary version of this paper was presented at SPAA 2006.     

Packing trees in communication networks

Given an undirected edge-capacitated graph and given (possibly) different subsets of vertices, we consider the problem of selecting a maximum (weighted) set of Steiner trees, each tree spanning a subset of vertices, without violating the capacity constraints. This problem is motivated by applications in multicast communication networks. We give an integer linear programming (ILP) formulation for the problem, and observe that its linear programming (LP) relaxation is a fractional packing problem with exponentially many variables and a block (sub-)problem that cannot be solved in polynomial time. To this end, we take an r-approximate block solver (a weak block solver) to develop a (1−ε)/r-approximation algorithm for the LP relaxation. The algorithm has a polynomial coordination complexity for any ε∈(0,1). To the best of our knowledge, this is the first approximation result for fractional packing problems with only weak block solvers (with arbitrarily large approximation ratio) and a coordination complexity that is polynomial in the input size. This leads also to an approximation algorithm for the underlying tree packing problem. Finally, we extend our results to an important multicast routing and wavelength assignment problem in optical networks, where each Steiner tree is to be assigned one of a limited set of given wavelengths, so that trees crossing the same fiber are assigned different wavelengths. A preliminary version of this paper appeared in the Proceedings of the 1st Workshop on Internet and Network Economics (WINE 2005), LNCS, vol. 3828, pp. 688–697. Research supported by a MITACS grant for all the authors, an NSERC post doctoral fellowship for the first author, the NSERC Discovery Grant #5-48923 for the second and fourth author, NSERC Discovery Grant #15296 for the third author, the Canada Research Chair Program for the second author, and an NSERC industrial and development fellowship for the fourth author.     

大数据中的管理问题: 基于大数据的资源观

作为重要的战略资源,大数据中包含诸多关键的管理问题． 文章首先评述了基于不同视角对大数据的认识． 然后,从管理的视角看大数据,指出大数据是一类重要的战略性信息资源,并从复杂性、决策有用性、高速增长性、价值稀疏性、可重复开采性和功能多样性等6 个方面探究了大数据资源的管理特征． 最后,提炼并探讨了大数据资源的获取问题、加工问题、应用问题、产权问题、产业问题和法规问题等6 个方面的关键管理问题     

Siting Bio-Samplers in Buildings

We describe a probabilistic approach to siting samplers for detecting accidental or intentional releases of biological material. In the face of uncertainty and variability in the release conditions, we place samplers in order to maximize the probability of detecting a release from among a suite of realistic scenarios. The scenarios may differ in any unknown, for example, the release size or location, weather, mode of building operation, etc. In an illustrative example, we apply the algorithm to a hypothetical 24-room commercial building, finding optimal networks for a variety of assumed sampler types. The results show how sampler characteristics, most importantly the detection limit, affect the network performance. This suggests using the probabilistic approach to guide the priorities of sampler designers, as well as to site samplers in specific buildings.