Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion. Our proposed framework systematically adapts Web complexity based on geographic variations in mobile broadband prices and income levels. We conduct a cross-country analysis of 99 countries, showing that our framework can better balance affordability and webpage quality while preserving user privacy. To adapt Web complexity, our framework solves an optimization problem to produce webpages that maximize page quality while reducing the webpage to a given target size.

5G and next-generation cellular networks aim to support tactile internet to enable immersive and real-time applications by providing ultra-low latency and extremely high reliability. This imposes new requirements on the design of cellular core networks. A key component of the cellular core is the control plane. Time to complete cellular control plane operations (e.g., mobility handoff, service establishment) directly impacts the delay experienced by end-user applications. In this paper, we design Neutrino, a cellular control plane that provides users an abstraction of reliable access to cellular services while ensuring lower latency. Our testbed evaluations based on real cellular control traffic traces show that Neutrino provides an improvement in control procedure completion times by up to$3.1\times $without failures, and up to$5.6\times $under control plane failures, over existing 5G. We also show how these improvements translate into improving end-user application performance: for AR/VR applications and self-driving cars, Neutrino improves performance by up to$2.5\times $and$2.8\times $, respectively.

The Internet of Things (IoT) ecosystem is witnessing widespread deployments for emerging applications in diverse domains such as remote sensing, smart homes, and industry 4.0. There is also a growing need to secure such deployments against malicious IoT devices to sustain normal network operations. Since the IoT deployments encompass geographically distributed nodes, blockchain technology, which inherently offers distributed trust in such scenarios, is gaining popularity in providing a secure and trusted IoT deployment. In this paper, we present a use case in which an IoT deployment is retrofitted with a blockchain. The use of blockchain prevents malicious nodes from falsifying information about their energy levels. We first present attack scenarios where IoT nodes can spoof energy while joining or being a part of the network. We then build a defense strategy and evaluate its performance under various attack scenarios. Our results indicate that the IoT deployment is robust under the proposed defense strategy which can detect if a node is spoofing its energy levels over 75% of the time.

Blockchain technology is finding applications in wireless networks, in particular vehicular networks, for the purposes of establishing trust. A certain set of network resources in terms of communication and computation requirements is also necessary for successful blockchain deployment. Moreover, heterogeneity in wireless networks and changing radio conditions at the physical layer, make it even more challenging to guarantee steady block generation latency. In this work, we study the possible denial of blockchain service attacks where malicious nodes threaten to slow down the block generation latency. We propose a novel Proof-of-Communication-Capability (PoCC) authentication framework that acts as a defense against communication capability spoofing over wireless networks. Our PoCC authentication framework utilizes physical properties such as distance between nodes, channel state information (CSI), and communication puzzle latency to establish the communication capabilities of nodes in the wireless network. Results from a simulated AV network under three different variations of the proposed PoCC framework are encouraging and demonstrate that such attacks can be effectively mitigated.

5G networks aim to provide ultra-low latency and higher reliability to support emerging and near real-time applications such as augmented and virtual reality, remote surgery, self-driving cars, and multi-player online gaming. This imposes new requirements on the design of cellular core networks. A key component of the cellular core is the control plane. Time to complete control plane operations (e.g. mobility handoff, service establishment) directly impacts the delay experienced by end-user applications. In this paper, we design Neutrino, a cellular control plane that provides users an abstraction of reliable access to cellular services while ensuring lower latency. Our testbed evaluations based on real cellular control traffic traces show that Neutrino provides an improvement in control procedure completion times by up to 3.1x without failures, and up to 5.6x under control plane failures, over existing cellular core proposals. We also show how these improvements translate into improving end-user application performance: for AR/VR applications and self-driving cars, Neutrino performs up to 2.5x and up to 2.8x better, respectively, as compared to existing EPC.

Existing flow scheduling schemes for data center networks optimize for a specific workload and performance metric. In this paper, we present 2D, a new scheduling policy that offers robustness across performance metrics and changing workloads - a ground existing scheduling policies are unable to cover. 2D combines basic scheduling building blocks of multiplexing and serialization in a principled way, ensuring tail optimal performance across workloads while also improving the average (and lower percentiles) completion times.

IoT deployments often require communication between devices that employ heterogeneous wireless technologies. Traditionally, expensive gateways are used to relay packets between heterogeneous nodes. Recent cross-technology communication offers a low bandwidth alternative, which is only feasible when communication between such nodes is limited to simple binary commands. In contrast, our work capitalizes on the increasing presence of multi-standard radio chips in mainstream IoT devices, to provide a new perspective on how to enable direct communication between heterogeneous nodes. We design Scylla---a software control layer---that allows multiple wireless stacks to coexist on top of a single radio chip, thereby simultaneously offering multiple communication interfaces. Uniquely, Scylla achieves near stack-native performance and requires no changes to the standards.

We present C-Saw, a system that measures Internet censorship by offering data-driven censorship circumvention to users. The adaptive circumvention capability of C-Saw incentivizes users to opt-in by offering small page load times (PLTs). As users crowdsource, the measurement data gets richer, offering greater insights into censorship mechanisms over a wider region, and in turn leading to even better circumvention capabilities. C-Saw incorporates user consent in its design by measuring only those URLs that a user actually visits. Using a cross-platform implementation of C-Saw, we show that it is effective at collecting and disseminating censorship measurements, selecting circumvention approaches, and optimizing user experience. C-Saw improves the average PLT by up to 48% and 63% over Lantern and Tor, respectively. We demonstrate the feasibility of a large-scale deployment of C-Saw with a pilot study.

Online advertising plays a critical role in enabling the free Web by allowing publishers to monetize their services. However, the rise in internet censorship events globally poses an economic threat to the advertising ecosystem. This paper studies this interplay and presents Advention, a system that provides censorship circumvention while serving relevant ads. Advention leverages the observation that ad systems are usually hosted on domains that are different from the publisher domains and are almost always uncensored. Taking cue from this, Advention fetches ads via the direct, uncensored, channel between users and the ad system. Preliminary results show that Advention not only offers high ad relevance compared to other popular relay-based circumvention tools, it also offers smaller page load times.

We propose dynamic reconfiguration of the radio interface in IoT platforms to support multiple link layers simultaneously. This allows us to tackle the increasing link layer heterogeneity in IoT devices, thus bringing a multitude of benefits: extensible and vender agnostic multihop deployments, rapid integration of the new “things” into an existing network, as well as seamless integration of the IoT with the traditional wireless Internet.

TCP incast congestion happens in many-to-one communication workflow patterns that frequently arise in large-scale datacenter applications such as web search, social networks, and cluster-based storage systems. Incast congestion can severely degrade the performance of applications. This paper studies the effectiveness of randomizing the TCP retransmission timeout (RTO) in mitigating the impact of incast. Our design is based on the observation that under incast, retransmitted packets also get synchronized due to the use of similar RTOs by the senders. Using analysis and experimental evaluation, we show that there exists a tradeoff between the randomization interval (from which the RTO values are picked) and the number of senders involved in incast. Motivated by this insight, we propose three algorithms (TDA, MAA, and FSA) for the dynamic adaptation of the randomization interval that rely on (a) successive timeouts, (b) explicit knowledge of the level of multiplexing, and/or (c) the knowledge of flow sizes (i.e., large interval for long flows and a small interval for short flows), respectively. Our results show that these algorithms improve goodput by 1.5x-11x for up to 64 senders and provide greater improvement for larger number of senders. The proposed algorithms can be readily deployed as they do not require any changes in switches or applications.

Existing research on Internet censorship primarily focuses on either measurements or circumvention. Considering these two in isolation often leads to designs with limited capabilities: Circumvention is not driven by measurement data and end users find little incentive to help gather such data. We present the preliminary design and implementation of C-Saw, a platform that offers both. The circumvention capability of C-Saw incentivizes consumers to opt-in. As more and more consumers crowdsource, the monitoring data gets richer. This, in turn, offers greater insights into the censorship mechanisms over a wider region, offering even better circumvention capabilities. C-Saw is a browser-based platform set up as a lightweight client-side proxy. C-Saw adapts its circumvention approach to the particular censorship mechanism deployed by a user's ISP, achieving a better balance of circumvention effectiveness and performance. In addition, it can also leverage the heterogeneity in filtering mechanisms across ISPs to achieve better circumvention performance. We demonstrate this using page load times across various ISPs and locations in a censored region. Unlike previous measurement approaches, C-Saw does not require the knowledge of a target URL to be tested. In fact, as URLs get blocked, their information can be monitored in real time.

Internet censorship artificially changes the dynamics of resource production and consumption, affecting a range of stakeholders that include end users, service providers, and content providers. We analyze two large-scale censorship events in Pakistan: blocking of pornographic content in 2011 and of YouTube in 2012. Using traffic datasets collected at home and SOHO networks before and after the censorship events, we: a) quantify the demand for blocked content, b) illuminate challenges encountered by service providers in implementing the censorship policies, c) investigate changes in user behavior (e.g., with respect to circumvention) after censorship, and d) assess benefits extracted by competing content providers of blocked content.

Data center operators face extreme challenges in simultaneously providing low latency for short flows, high throughput for long flows, and high burst tolerance. We propose a buffer management strategy that addresses these challenges by isolating short and long flows into separate buffers, sizing these buffers based on flow requirements, and scheduling packets to meet different flow-level objectives. Our design provides new opportunities for performance improvements that complement transport layer optimisations.

Today router buffers are sized according to the well-known Bandwidth-Delay Product (BDP) rule, which uses the average round-trip time (RTT) of flows traversing a router. The BDP rule not only leads to large queueing delays, but also imposes “one (buffer) size fits all” philosophy for flows exhibiting a large variation in RTT. When short and long RTT flows compete at a single buffer, they may adversely affect each other in throughput and delay. We propose SplitBuff, a scheme using which short RTT flows achieve low delay and long RTT flows achieve high throughput, without requiring any protocol modifications. With SplitBuff, a router splits a buffer into multiple buffers of varying sizes and maps flows onto these buffers based on their RTTs. We describe SplitBuff and evaluate its performance using extensive ns-2 simulations to demonstrate its effectiveness.

In this thesis, we formulate a generalized optimization problem to minimize the electricity cost of network operation while using techniques applicable to operational networks. We applied our optimization formulation to two different networks: geo-diverse data centers and cellular networks. In case of cellular networks, using traffic traces from an large operational network, we observed that an operator can save up to 22% in electricity costs by using our proposed scheme. While the percentage amount seems modest, for an operator with 7000 cellular sites in an urban setting, this translates to savings of up to 35.36 MWh annually.

For provisioning large-scale online applications such as web search, social networks and advertisement systems, data centers face extreme challenges in providing low latency for short flows (that result from end-user actions) and high throughput for background flows (that are needed to maintain data consistency and structure across massively distributed systems). We propose L2DCT, a practical data center transport protocol that targets a reduction in flow completion times for short flows by approximating the Least Attained Service (LAS) scheduling discipline, without requiring any changes in application software or router hardware, and without adversely affecting the long flows. L2DCT can co-exist with TCP and works by adapting flow rates to the extent of network congestion inferred via Explicit Congestion Notification (ECN) marking, a feature widely supported by the installed router base. Though L2DCT is deadline unaware, our results indicate that, for typical data center traffic patterns and deadlines and over a wide range of traffic load, its deadline miss rate is consistently smaller compared to existing deadline-driven data center transport protocols. L2DCT reduces the mean flow completion time by up to 50% over DCTCP and by up to 95% over TCP. In addition, it reduces the completion for 99th percentile flows by 37% over DCTCP. We present the design and analysis of L2DCT, evaluate its performance, and discuss an implementation built upon standard Linux protocol stack.

Cloud infrastructure providers and data center operators spend a major portion of their operations budget on the electric bills. We present RED-BL (Relocate Energy Demand to Better Locations), a framework for determining an optimal mapping of workload to an existing set of data centers while considering the cost of workload relocation. Within each workload mapping interval, RED-BL solution exploits the geo diversity in electricity price markets. The temporal diversity in those markets is simultaneously exploited by considering a planning window comprising several mapping intervals. Using workload traces from live Internet applications and electricity prices from the US markets, RED-BL can reduce the electric bill by as much as 81% from the case when the workload is equally distributed. Compared to a single data center deployment, an average reduction of 27% in electric bill can be achieved when RED-BL uses 10 or more data centers, a common case for most operators. When compared to existing workload relocation solutions, RED-BL achieves a further reduction of 13.63%, on average. While modest, this reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window.

The ability of tiny embedded devices to run large feature-rich programs is typically constrained by the amount of memory installed on such devices. Furthermore, the useful operation of these devices in wireless sensor applications is limited by their battery life. This paper presents a call stack redesign targeted at an efficient use of RAM storage and CPU cycles by a Java program running on a wireless sensor mote. Without compromising the application programs, our call stack redesign saves 30% of RAM, on average, evaluated over a large number of benchmarks. On the same set of bench-marks, our design also avoids frequent RAM allocations and deallocations, resulting in average 80% fewer memory operations and 23% faster program execution. These may be critical improvements for tiny embedded devices that are equipped with small amount of RAM and limited battery life. However, our call stack redesign is equally effective for any complex multi-threaded object oriented program developed for desktop computers. We describe the redesign, measure its performance and report the resulting savings in RAM and execution time for a wide variety of programs.

IP Routers use sophisticated forwarding table (FIB) lookup algorithms that minimize lookup time, storage, and update time. This paper presents SMALTA, a practical, near-optimal FIB aggregation scheme that shrinks forwarding table size without modifying routing semantics or the external behavior of routers, and without requiring changes to FIB lookup algorithms and associated hardware and software. On typical IP routers using the FIB lookup algorithm Tree Bitmap, SMALTA shrinks FIB storage by at least 50%, representing roughly four years of routing table growth at current rates. SMALTA also reduces average lookup time by 25% for a uniform traffic matrix. Besides the benefits this brings to future routers, SMALTA provides a critical easy-to-deploy one-time benefit to the installed base should IPv4 address depletion result in increased routing table growth rate. The effective cost of this improvement is a sub-second delay in inserting updates into the FIB once every few hours. We describe SMALTA, prove its correctness, measure its performance using data from a Tier-1 provider as well as Route-Views. We also describe an implementation in Quagga that demonstrates its ease of implementation.

Semantically equivalent IP prefix tables exhibit the same forwarding behavior and, thus, can be used in place of each other for the purpose of packet forwarding. Researchers, service providers, and equipment vendors often need to establish this semantic equivalence of prefix tables. Given the large number of IP addresses in use today, either (i) this task takes too long to complete on any available hardware, or (ii) one has to resort to approximation by looking up, in the two tables, only a sample of IP addresses from the entire IP space. We present TaCo (Table Comparison), an algorithm to determine whether or not two given IP prefix tables are semantically equivalent. TaCo is efficient yet accurate, as it guarantees the equivalence result over the entire IP space without explicitly performing a lookup on each IP address over this space. We show that the number of longest prefix matching lookups TaCo needs to perform is bounded by a number that is linear in the sum of the number of entries in the two tables. In practice, for the existing prefix tables in large service providers, the number of longest prefix matchings needed by TaCo does not exceed a few hundred thousand entries.

The ability of tiny embedded devices to run large and feature-rich Java programs is typically constrained by the amount of memory installed on those devices. Furthermore, the useful operation of such devices in a wireless sensor application is limited by their battery life. We propose a garbage collection (GC) scheme called Offline GC which alleviates both these limitations. Our approach defies the current practice in which an object may be deallocated only if it is unreachable. Offline GC allows freeing an object that is still reachable but is guaranteed not to be used again in the program. Furthermore, it may deallocate an object inside a function, a loop or a block where it is last used, even if that object is assigned to a global field. This leads to a larger amount of memory available to a program.

IEEE 802.11 distributed coordination function (DCF) uses a binary exponential backoff algorithm to arbitrate simultaneous channel access by multiple stations. When a station senses that either the shared wireless channel is busy or a collision has occurred, that station must delay the transmission by backoff time. This backoff time, in legacy DCF, is uniformly selected from a common contention window range that, in turn, ensures equal chance of grabbing the wireless medium by each station. Therefore, legacy DCF does not support service differentiation or priority access to the channel. To support service differentiation and increased quality of service (QoS), we propose two enhancements to the procedures used for managing contention window in original protocol. These two enhancements can be easily incorporated within the legacy DCF mechanism. First, instead of a common window for all classes, each traffic class is assigned a contention window with minimum and maximum values which are specific to that class. Second, the backoff algorithm is adjusted to follow a multiple increase multiple decrease (MIMD) procedure. First enhancement provides service differentiation and the second improves performance for all traffic classes. Unlike previous schemes, our proposed scheme does not require protocol changes in the legacy DCF. We evaluate our scheme using extensive simulations and demonstrate that, in comparison with legacy DCF, our newly proposed scheme provides a throughput increase of about 30% for high priority flows and of about 20% for medium priority flows.

Ethernet is a low-cost, flexible and high-speed transport technology, which has traditionally seen success in local area networks and is rapidly gaining popularity in metro networks. However, its control plane, primarily based on spanning tree protocol, is not well-suited for the metro and core networks. For such networks, the IETF is evaluating a proposed framework called GELS which uses GMPLS as the control plane for Ethernet data plane. In this paper, we provide a quantitative assessment of GELS for service provider networks. In particular, we perform simulations using COST239 and COST266 networks to evaluate the performance of GELS under normal network conditions, as well as under failure conditions. Under normal network conditions, we find that the use of GELS results in placement of up to 46.4% more bandwidth when compared with native Ethernet control such as RSTP. In terms of LSP acceptance, GELS shows a 45.5% improvement over RSTP. Similarly, average link utilization using GELS is significantly better than the average link utilization when the native Ethernet control plane is used. When considering single element failures, RSTP recovers by converging to a potentially new spanning tree, which may take unacceptably long. In contrast, well-known restoration and protection mechanisms of GMPLS control plane result in much faster recovery. We And that the convergence times exhibited by GELS after single element failures are orders of magnitude better than those obtained when RSTP is used.

For label switched networks, such as MPLS and GMPLS, most existing traffic engineering (TE) solutions work in a single routing domain. These solutions do not work when a route from the ingress node to the egress node leaves the routing area or the autonomous system (AS) of the ingress node. In such cases, the path computation problem becomes complicated because of the unavailability of the complete routing information throughout the network. We present CWS (computation while switching), a new inter-domain path computation scheme which tries to compute a near-optimal path without assuming the availability of complete topology information. We provide a detailed comparison of the CWS scheme with another per-domain path computation scheme given in J.-P. Vasseur et al. (2006). Unlike the standard per-domain path computation scheme (Vasseur et al., 2006), the CWS scheme continues the quest for a better path instead of terminating the search at the first available path, resulting a significant improvement in terms of path optimality. In particular, CWS guarantees that, for a given network state, a computed inter-domain path will traverse a minimum number of domains. This improvement in path computation directly impacts the amount of traffic that can be allowed on the network. For example, for the COST266 topology with 28 domains and 37 bidirectional inter-domain links, CWS places 960 of the requested 2000 paths as compared to 683 paths placed by existing schemes. Finally, the path setup latency of the CWS scheme remains comparable to that of existing schemes, by allowing the data flow as soon as the first feasible path is found.

Block based motion compensation techniques are commonly used in video encoding to reduce the temporal redundancy of the signal. In these techniques, each block in a video frame is matched with another block in a previous frame. The match criteria normally used is the minimization of the sum of absolute differences (SAD). Traditional encoders take the difference of the current block and its best matching block, and this differential signal is used for further processing. Rather than directly encoding the difference between the two blocks, we propose that the difference between the current block and its first order linear estimate from the best matching block should be used. This choice of using linear compensated differential signal is motivated by observing frequent brightness and contrast changes in real videos. We show two important theoretical results: (1) The variance of the linear compensated differential signal is always less than or equal to the variance of differential signal in traditional encoders. (2) The optimal criteria for finding the best matching block, in our proposed scheme, is the maximization of the magnitude of correlation coefficient. The theoretical results are verified through experimentation on a large dataset taken from several commercial videos. For the same number of bits per pixel, our proposed scheme exhibits an improvement in peak signal to noise ratio (PSNR) of up to 5 dB when compared to the traditional encoding scheme.

We address the problem of connection preemption in a multi-class network environment. Our objective is: (i) to minimize the number of preempted connections, and (ii) to minimize the total preempted bandwidth, in that order. We show that this problem is NP-complete by reducing it to a well-known NP complete problem - the subset sum problem. Therefore, a known polynomial time algorithm, such as Minn.Conn [1], to solve this problem is suboptimal. We present an optimal algorithm with exponential complexity that can be used when the network load is light. We also present a fully polynomial time approximation algorithm that performs within a bounded factor from the optimal, and can be used in large networks having thousands of connections. We compare the performance of exact and approximate algorithms in a practical scenario by conducting simulations on a network representing twenty largest metros in the U.S. The simulations show that, on average, the approximate algorithm preempts bandwidth which is only a small fraction more as compared to that preempted by the exact algorithm, but is an order of magnitude more efficient in terms of execution time.

In label-switched networks such as MPLS, protection routing involves computing and setting up the backup paths at the same time when the primary paths are routed. It has previously been shown that two or more backup paths may share bandwidth along common links if such backup paths will never be activated simultaneously. Such sharing between the backup paths leads to reduced bandwidth reservations and, hence improved performance in terms of number of path requests that can be accommodated on the network (Aslam et al., 2005). We present a novel idea that backup paths may also share bandwidth with certain primary paths, thereby further reducing the overall bandwidth reservations on the network. This results in even more path requests being accommodated on the network. Sharing with primary paths is possible with any protection routing framework. To demonstrate this sharing, we use the NPP protection routing framework as an example (Aslam et al., 2005). We provide the enhancements to the NPP framework needed to exploit sharing with the primary paths. For the enhanced NPP framework, simulation results on various networks confirm that sharing with primary paths indeed results in better network utilization. This increased performance is achieved with bounded local state information and without requiring any additional routing or signaling overhead.

We investigate the problem of distributed online routing of bandwidth guaranteed paths with local restoration. A unified model is proposed that captures the bandwidth sharing characteristic of backup paths that provision local restoration, corresponding to different fault models. We apply the model to describe bandwidth sharing on backup paths for varying degrees of network state information. The extent of backup bandwidth sharing depends on the amount of network state information made available through routing protocols. A key design criterion for traffic engineering schemes is to maximize the sharing between backup paths, while minimizing this protocol overhead. M.S. Kodialam and T.V. Lakshman (see Proc. Infocom, p.376-85, 2001) demonstrated that propagating a constant amount of aggregated information per link leads to cost effective bandwidth sharing. We propose oAIS, a new aggregate information scenario, in which we judiciously select the propagated information, such that the protocol overhead is identical to that of Kodialam and Lakshman. Simulations show that oAIS outperforms other information scenarios with comparable protocol overheads.

Mobility in wireless sensor networks poses unique challenges to the medium access control (MAC) protocol design. Previous MAC protocols for sensor networks assume static sensor nodes and focus on energy-effciency. In this paper, we present a mobility-adaptive, collision-free medium access control protocol (MMAC) for mobile sensor networks. MMAC caters for both weak mobility (e.g., topology changes, node joins, and node failures) and strong mobility (e.g., concurrent node joins and failures, and physical mobility of nodes). MMAC is a scheduling-based protocol and thus it guarantees collision avoidance. MMAC allows nodes the transmission rights at particular timeslots based on the traffic information and mobility pattern of the nodes. Simulation results indicate that the performance of MMAC is equivalent to that of TRAMA [1] in static sensor network environments. In sensor networks with mobile nodes or high network dynamics, MMAC outperforms existing MAC protocols, like TRAMA and S-MAC [2], in terms of energy-efficiency, delay, and packet delivery.

We present AMOR, a new scheme for on-demand routing in wireless ad hoc networks. In ad hoc networks, mobile nodes are connected to each other such that the connection pattern changes dynamically with the mobility of nodes. Therefore, classical table driven routing algorithms necessitate updated network state information to be periodically disseminated, and incur significant costs in terms of network utilization, memory, and associated update-processing. In contrast, on-demand routing algorithms do not maintain pre-computed routes and real time route discovery ensues when data traffic needs to be transmitted between a source-destination pair. AMOR employs a modified version of the Dynamic Source Routing protocol to discover multiple node-disjoint paths between a source-destination pair. A key feature of AMOR is that instead of computing minimum-hop paths, it computes paths such that a measure of the 'transmission reliability', namely ETX, between the source-destination pair is optimized. Furthermore, AMOR employs a real time loading algorithm that optimally load balances traffic across multiple paths. The control traffic overhead in AMOR is comparable to that of single-path on-demand protocols. We present analytical throughput results in a simplified AMOR model of a network of multi-radio nodes. We also conduct simulation experiments to study the throughput performance of the AMOR scheme in a network of single-radio nodes. Our results show that AMOR outperforms the traditional dynamic source routing algorithm.

Content aware networking is a new paradigm in which the network is intelligent to understand the type of content request, where to find it, and how to deliver it in an efficient way. We discuss, why the existing IP-smart paradigm does not meet the needs of today's content rich Internet. We describe content aware networking issues that include content storage across distributed servers, content based routing, content delivery, content distribution and content based resource reservation. Content delivery is often associated with multicast and we present various multicast techniques by which a content can be delivered efficiently. The emerging end-to-end IP security (IPSec) poses some unique challenge to this trend and we present few solutions to overcome the problem.

A sub-symbol based multiuser detector for DS-CDMA systems was proposed in Uzmi and Mujtaba (1999). This paper addresses the optimization of such detectors to maximize the system capacity. It is shown that the maximum number of simultaneous users supported in a DS-CDMA system using a sub-symbol scheme can be increased by the proper selection of certain thresholds delineated in Uzmi and Mujtaba, and by imposing moderate synchronization requirements on the transmitters. It is also shown that, in a synchronized environment, performing with only one sub-symbol is enough for accommodating a fairly large number of users, if the system signal to noise ratio (SNR) is medium to high. It has been verified through simulations that the maximum number of simultaneous users depends on the degree of synchronism and is approximately given by the number of chips in the longest sub-symbol.