All analytical and simulation research on ad hoc wireless networks must necessarily model radio propagation using simplifying assumptions. We provide a comprehensive review of six assumptions that are still part of many ad hoc network simulation studies, despite increasing awareness of the need to represent more realistic features, including hills, obstacles, link asymmetries, and unpredictable fading. We use an extensive set of measurements from a large outdoor routing experiment to demonstrate the weakness of these assumptions, and show how these assumptions cause simulation results to differ significantly from experimental results. We close with a series of recommendations for researchers, whether they develop protocols, analytic models, or simulators for ad hoc wireless networks.Mobile ad hoc networking (MANET) has become a lively field within the past few years. Since it is difficult to conduct experiments with real mobile computers and wireless networks, nearly all published MANET articles are buttressed with simulation results. Many such simulations may be based on overly simplistic assumptions, however; a recent article in IEEE Communications warns that "An opinion is spreading that one cannot rely on the majority of the published results on performance evaluation studies of telecommunication networks based on stochastic simulation, since they lack credibility" [10]. It then proceeded to survey 2200 published network simulation results to point out systemic flaws.Although not every simulation study needs to use the most detailed radio model available, nor explore every variation in the wide parameter space afforded by a complex model, there are real risks to protocol designs based on overly simple models of radio propagation. As the nodes move in an ad hoc network, the connectivity graph changes over time, but these changes depend significantly on variations in the range due to antenna differences, elevation (extending range), and obstacles (restricting range).We recognize that the MANET research community is increasingly aware of the limitations of its simplifying assumptions. Our goal in this paper is to make a constructive contribution to the MANET community by a) clearly identifying these assumptions and quantitatively demonstrating their weaknesses, b) comparing simulation results to experimental results to identify how simplistic radio models can lead to misleading results in ad hoc network research, c) contributing a real dataset that should be easy to incorporate into simulations, and d) listing recommendations for the designers of protocols, models, and simulators.Due to limited space, this paper presents only the main points. We present all of the details in an extended Technical Report [6]. MODELS USED IN RESEARCHThe simplest radio models are based on distance across flat terrain; radio communications are received perfectly within some circular "range" and not at all outside of that range. Real radios, including those used in the popular Berkeley Motes, demonstrate a strikingly non-uniform non-...

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A mobile agent is an executing program that can migrate during execution from machine to machine in a heterogeneous network. On each machine, the agent i n teracts with stationary service agents and other resources to accomplish its task. Mobile agents are particularly attractive in distributed informationretrieval applications. By moving to the location of an information resource, the agent can search the resource locally, eliminating the transfer of intermediate results across the network and reducing end-toend latency. In this chapter, we rst discuss the strengths of mobile agents, and argue that although none of these strengths are unique to mobile agents, no competing technique shares all of them. Next, after surveying several representative mobile-agent s y s t e m s , w e examine one speci c information-retrieval application, searching distributed collections of technical reports, and consider how mobile agents can be used to implement this application e ciently and easily. Then we spend the bulk of the chapter describing two planning services that allow mobile agents to deal with dynamic network environments and information resources: (1) planning algorithms that let an agent c hoose the best migration path through the network, given its current task and the current network conditions, and (2) planning algorithms that tell an agent h o w to observe a c hanging set of document s i n a w ay that detects changes as soon as possible while minimizing overhead. Finally, we consider the types of errors that can occur when information from multiple sources is merged and ltered, and argue that the structure of a mobile-agent application determines the extent to which these errors a ect the nal result.

Most comparisons of wireless ad hoc routing algorithms involve simulated or indoor trial runs, or outdoor runs with only a small number of nodes, potentially leading to an incorrect picture of algorithm performance. In this paper, we report on an outdoor comparison of four different routing algorithms, APRL, AODV, ODMRP, and STARA, running on top of thirty-three 802.11-enabled laptops moving randomly through an athletic field. This comparison provides insight into the behavior of ad hoc routing algorithms at larger real-world scales than have been considered so far. In addition, we compare the outdoor results with both indoor ("tabletop") and simulation results for the same algorithms, examining the differences between the indoor results and the outdoor reality. Finally, we describe the software infrastructure that allowed us to implement the ad hoc routing algorithms in a comparable way, and use the same codebase for indoor, outdoor, and simulated trial runs.

Abstract. Mobile-agent systems must address three security issues: protecting an individual machine, protecting a group of machines, and protecting an agent. In this chapter, we discuss these three issues in the context of D'Agents, a mobile-agent system whose agents can be written in Tcl, Java and Scheme. (D'Agents was formerly known as Agent Tcl.) First we discuss mechanisms existing in D'Agents for protecting an individual machine: (1) cryptographic authentication of the agent's owner, (2) resource managers that make policy decisions based on the owner's identity, and (3) secure execution environments for each language that enforce the decisions of the resource managers. Then we discuss our planned market-based approach for protecting machine groups. Finally we consider several (partial) solutions for protecting an agent from a malicious machine.