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Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O (V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O(V2 + VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2.

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Again, this de nition also applies to scheduling without communication costs 611 List Scheduling with Node Insertion Even though employing the insertion technique in list scheduling with start time minimization has the potential to reduce the schedule length in comparison to the end technique, there is in general no guarantee of improvement This becomes clear when one realizes that the insertion technique might lead to other processor allocations than the end technique For instance, with the insertion technique node g is scheduled on P2 in the example of Figure 61; with the end technique it would be allocated to either P1 or P2 As a consequence, communications might be remote, when they would be local with the end technique, resulting in a longer schedule However, with the same node order and processor allocation the insertion technique is at least as good as the end technique This is formulated in the next theorem, which is an extension of Theorem 51 of Section 51 with an almost identical proof Theorem 61 (Insertion Better than End Technique) Let S be a feasible schedule for task graph G = (V, E, w, c) on system P Using simple list scheduling (Algorithm 9), with the nodes scheduled in nondecreasing order of their start times in S and allocated to the same processors as in S, two schedules are created: Send , employing the end technique (De nition 52) and Sinsert employing the insertion technique (De nition 1) Then, sl(Sinsert ) sl(Send ) sl(S) (63). Bar Code In VS .NET Using Barcode generator for .NET framework Control to generate, create bar .Related: 

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Using Barcode printer for VS NET Control to generate, create QR ode JIS X 0510 image in NET framework applications Using Barcode decoder for NET framework Control to read scan read, scan image in NET applications A technique similar to SeRLoc is used in HiRLoc [24 which has a greater accuracy but an increased computational and communication complexity Techniques like these can be used to protect the Localization Algorithm Component Another simple and secure algorithm is the already-mentioned Localization with a Mobile Beacon (MBL) [23] Although MBL is not designed to be a secure algorithm and the authors do not mention anything about security in their work, the algorithm used in the MBL is quite simple, where a mobile beacon walks the network and broadcasts its position information to near nodes.

In this case, regular nodes only need to listen to the beacon node and they never exchange messages among themselves By being a simple single-hop algorithm, this localization system is secured against a number of distributed attacks such as the wormhole attack 1846 Comparison of Current Solutions A widely known fact in network security is that there is no system that is totally safe and secure There will always be weak points and the question is simply whether they are acceptable In WSNs, this issue becomes a little more complicated due to resource limitations In this case, we need to decide on the required level of security, which is application-dependent, and how many resources can be spent in providing these levels of security Depending on this cost bene t analysis, we can decide which solution or what security techniques will be used to secure the WSN.

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In Table 181, we compare each of the studied proposals, showing which type of security they use as well as some observations about them and their potential weaknesses As we can see, most security proposals rely on some kind of lightweight cryptography as a second line of defense combined with other security techniques such as misbehavior detection, robust position computation, location veri cation, and simple algorithms combined with extra hardwareRelated: .

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