Wireless Channel State-Aware and Adaptive Epidemic ... - Springer Link

Int J Wireless Inf Networks (2014) 21:58–67 DOI 10.1007/s10776-013-0232-6

Wireless Channel State-Aware and Adaptive Epidemic Dissemination in Ad Hoc Networks Theofanis Kontos • Christos Anagnostopoulos Stathes Hadjiefthymiades

•

Received: 28 September 2012 / Accepted: 25 September 2013 / Published online: 9 October 2013 Springer Science+Business Media New York 2013

Abstract Ad hoc networks are characterized by limited resources (e.g. energy, bandwidth). Efficient information dissemination while avoiding excessive energy cost can be achieved through the suitable design of a network. To this end we propose an information dissemination scheme which couples epidemic dissemination with adaptive modulation and coding (AMC). The proposed scheme tunes the message forwarding probability and the coding and modulation mode in order to achieve a balance between maximum coverage over the network and minimum energy expenditure. We achieve this based on the evaluation of suitably defined indicators related to the lower network layers and exploiting information on the current status of the wireless medium. Building on established previous AMC-related work, our simulation results indicate that our scheme brings significant improvement over non-adaptive approaches, comparable with other adaptive epidemic dissemination schemes. Our findings are quite promising for adaptive epidemic-based information dissemination schemes with a strong crosslayer component.

Keywords Epidemic information dissemination Adaptive probabilistic information dissemination Ad hoc networks Adaptive modulation and coding

1 Introduction Ad hoc networks are characterized by the strong need to rationalize the usage of scarce resources in order to elongate network lifetime. We focus on disseminating information upon a number of ad hoc-networked wireless nodes efficiently and with low energy cost. We assume that: •

• •

•

There is a single piece of information that we desire all nodes to obtain. To this end all possessing nodes (termed infected nodes) broadcast it probabilistically. We want as many nodes as possible to be infected. The transmission and reception energy cost is known, while the cost of computing is considered negligible in comparison. Nodes receive and broadcast information using an adaptive modulation and coding (AMC) scheme. They can switch at no time cost between different modes of the AMC. Our objective is to keep energy cost as low as possible without compromising network coverage.

T. Kontos (&) S. Hadjiefthymiades National and Kapodistrian University of Athens, Athens, Greece e-mail: [email protected]

•

S. Hadjiefthymiades e-mail: [email protected]

To this end we propose a context-aware, adaptive epidemic information dissemination scheme. It functions in cooperation with AMC and exploits the awareness of the channel status. It tunes the message forwarding probability and the coding mode utilizing information from the lower layers as input. At every discrete time instance, a certain metric is evaluated and then a combination of coding mode and forwarding probability values is selected for the upcoming period (temporal iteration). A decision to change

C. Anagnostopoulos Ionian University, Corfu, Greece e-mail: [email protected] Present Address: C. Anagnostopoulos School of Computing Science, University of Glasgow, Glasgow, UK

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Int J Wireless Inf Networks (2014) 21:58–67

or keep the values of one or both of these parameters constitutes a policy. The policy chosen is the one that yields the highest possible metric value for the given conditions. The purpose of our work is to achieve high network coverage without excessive energy cost due to redundant transmissions or unnecessarily high error control code (ECC) overhead. Our paradigm has two degrees of freedom; epidemic dissemination tuning is brought into effect to assist the AMC and vice versa. It exploits the flexibility provided by this combination, while remaining energy-aware. It is known that epidemic dissemination [1, 2, 3] achieves lower energy cost compared to flooding [4, 5]; our objective is to extend the adaptive nature of epidemic dissemination [6, 7]. The rest of the paper is structured as follows. Section 2 presents previous work on adaptive information dissemination schemes. In Sect. 3, we elaborate on the rationale of our proposal together with an analysis of our scheme. Section 4 shows performance metrics for evaluating the model as well as simulation results. Finally, conclusions and future work are discussed in Sect. 5.

2 Related Work Redundant transmissions in ad hoc networks, where nodes do not have global topology knowledge, are a significant energy-consumption factor. To overcome this issue, it has long been suggested that nodes apply selective message forwarding schemes depending on local conditions [1, 2, 8, 9]. These are used to help nodes adapt the way they disseminate data. Knowledge on such conditions may be obtained through either active or passive methods. In the former case, a node typically actively queries its neighbors on specific metrics. In the negotiation-based adaptive epidemic scheme in [1] the nodes, which are about to forward data (referred to as infected nodes) poll their neighbors about their residual energy before adjusting their message forwarding probability. Moreover, there are approaches that select a subset of neighboring nodes prior to information forwarding. Such selection schemes are also based on local information. The publish-subscribe paradigm in [2] assists in filtering some nodes out of the potential information receivers. The model proposed in [10] foresees quench waves, consisting of messages of a protocol whose specific purpose is to limit redundant transmissions. One notices that the active schemes described so far require the explicit co-operation of neighbor nodes. However, a trade-off needs to be defined here, as nonredundant traffic may be affected too. The model in [8] is based on the adaptation of the forwarding probability on a node w.r.t. the (local) network density, based on the concept that, over-frequent transmissions are unnecessary in a dense network. In [9], a significant amount of energy is

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saved by regulating the transmission range of the wireless node. In this manner, the probability that an infected node ‘infects’ a ‘susceptible’ one is effectively adapted. Passive schemes do not impose extra transmission energy cost. We contribute to the field of adaptive epidemic-based information dissemination schemes by proposing a passive scheme, based on local decisions. Neighboring nodes are not polled on their residual energy or infection state. Information forwarding among neighboring nodes is probabilistic. The forwarding probability and the coding mode are used to calculate the probability of successful message reception by neighbors. Modifying the former parameters simultaneously with each other impacts this probability. The values that promote the successful reception of the information without incurring too high energy cost are considered more favorable. It is intuitive that increasing the forwarding probability counterbalances loss due to high channel noise. Moreover, more efficient coding scheme from the ones available by the AMC assist in mitigating the effects of corrupted data delivery. This component of cross-layer awareness builds upon the channel model and AMC in [11]. Our scheme combines the flexibility of the AMC with the efficiency of adaptive epidemics, thus, constituting a novel scheme with cross-layer features.

3 System Model The purpose of transmission characteristics adaptation is twofold, increasing network coverage by the infection, and reducing power consumption to increase network lifetime. The proposed scheme aims to reduce redundant transmissions as well as optimize ECC in order to increase the probability of receiving error-free information. In this section we explain our assumptions and solution. 3.1 Channel Model he wireless channel is assumed noisy according to the Nakagami-m fading channel model [12] with Gaussian noise. To mitigate the impact of noise, we adopt convolutional ECC. The packet error rate (PER) in wireless channels in the presence of convolutional ECC has been investigated in [11, 13], and our work builds upon this knowledge and adopts the model proposed there for the simulations. According to the AMC scheme adopted from [11], the signal-to-noise ratio (SNR) range within which the wireless interface of the network nodes operates is divided in a fixed number (six) of disjoint intervals. A modulation and coding mode of the ones available for this AMC scheme is used in each interval. The PER is approximated by Eq. (1) reproduced here from [11]. Clearly, the parameters cpn separate the (disjoint) SNR intervals as mentioned above. Symbol c

123

60


stands for the SNR. The parameters an and gn depend on the mode and their values are assumed from [11] ( PERðcÞ ¼

1; if an expðgn cÞ; if

0\c\cpn c cpn an expðgn cÞ;

if

c cpn

:

ð1Þ 3.2 Epidemic Model Information dissemination in the discussed network occurs in an epidemic manner [14]. This attempts to achieve reception of information by as many network nodes as possible by ‘‘infecting’’ them in a stochastic way. Nodes that do not carry information are assumed to be susceptible, whereas those that do are infected. An infected node attempts to infect others by further broadcasting the information it possesses. An infected node i disseminates the information at time t with a forwarding probability bi(t). The result is that an infected node’s susceptible neighbor gets infected with a probability Pinf. Moreover, the cure of an infected node can occur at some time once the carried (infecting) piece of information turns obsolete or unusable. Cure happens at a (cure) rate di(t) [ [0, 1] for a node i at time t. We adopt the susceptible–infected–susceptible model [14] that stipulates that an infected node that is cured becomes susceptible again with rate di(t) and may be further infected. 3.3 Network Model and Assumptions In our model the time domain is discrete and time assumes integer values. We assume an ad hoc network with N nodes. Some of them sense environmental parameters and the derived readings are diffused wirelessly into the network. The nodes have no or low mobility so that the topology changes slowly compared to the dynamics of the proposed algorithm. Each node is indexed with an integer value i [ N. The neighborhood of a node i is denoted by the set Vi and a node j is neighbor to node i iff j [ Vi, i.e. node j is within the communication range of node i. At time instance t, let I(t) and S(t) be the sets of nodes in the infected and the susceptible state respectively, with |I(t)| ? |S(t)| = N. |U| denotes the cardinality of the set U. It holds that 0 \ I(1) \ N. Nodes can switch between different AMC modes subject to the logic described in this paper and communications’ context. The computing overhead (and memory implications) is considered significantly lower to the packet transmission overhead [15]. There are two key parameters that we tune in our scheme, in order to improve the information dissemination efficiency: The encoding mode provided by the AMC, and the probability b, with which a node forwards the

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information it carries. We try to determine the best combination of the two as a more efficient ECC scheme improves noise resilience (and, therefore, delivery probability) at the expense of packet length while a more intense epidemic dissemination increases delivery probability with lower packet length but with duplicate transmissions. For each infected node i, there is a set of allowed discrete forwarding probability values from the set B = {B1, B2,…, B|B|}, Bk [ (0, 1]. All infected nodes at t = 1 initialize their forwarding probability bi(1) with some value b0 [ B, common for all i [ I(1). At time t, an infected node i forwards with probability bi(t) [ B. The quantity Db = Bk?1 - Bk, 1 B k B |B| - 1 is assumed constant and is termed the beta step. We assume the cure rate value to be equal to a fixed fraction of the current forwarding probability, so that di(t)/ bi(t) = k \ 1, where k is a constant. We also assume the constraint that the d takes values low enough for the epidemic to persist. For the coding mode li(t) of a mode i at time t for our network of N nodes, we specify: li ðtÞ 2 f1; 2; . . .; 6g;

li ð1Þ ¼ l0 8i 2 f1; 2; . . .N g:

ð2Þ

For each node i, the coding mode li(t) is dynamically selected from the values {1,2…, 6} corresponding to the modes explained in Sect. 3.1—depending on the value of the SNR ci(t - 1)—so that the following condition is satisfied: ( li ðtÞ þ 1; if ci ðtÞ ci ðt 1Þ li ðt þ 1Þ ¼ : ð3Þ li ðtÞ 1 if ci ðtÞ [ ci ðt 1Þ The rationale behind this mode tuning lies in the use of the AMC. Stronger/weaker coding is adopted corresponding to respective decrements/increments of the SNR so that error-free information reception can be promoted. Assume that we have an infected node i at time instance t. We establish the following model in terms of the epidemic infection: • • •

At each time t an infected node i stays infected with probability 1 - di(t). If it remains infected, it transmits the carried (infecting) information or not. This occurs with probability bi(t). The infected node attempts to disseminate the infection information to all neighboring nodes j [ Vi using broadcasting. The energy cost for the transmitting node is the cost of a single broadcast transmission. The cost of a single reception is incurred at each receiver, even if the received message is corrupted. Hence, if ETx and ERx is the cost for a single transmission and reception respectively, the transmitting node i spends energy equal to ERx and each of its |Vi| neighbors spends ETx. Then, the cost for the whole neighborhood is ETx ? |Vi|ERx.


•

61

Reception by each neighbor j [ Vi is successful when the received information is not corrupted due to the channel noise. The effect of this noise is modeled with the PER. Successful reception occurs with success probability equal to 1-PER, where PER is the packet error rate dictated by the SNR perceived for the communication between i and j.

According to the aforementioned model, the probability that the information, which the infected node i carries, is successfully received by at least one neighbour j [ Vi is: Psr ¼ ð1 dÞbð1 PERn Þ;

ð4Þ

where n = |Vi| is the number of neighbours and PER is the packet error rate due to the perceived SNR. It should be stressed that this equation does not exempt the successful reception by already infected nodes (redundant receptions), thus, it includes redundant communication too. The probability Psr that at least one uninfected (susceptible) neighbour gets infected caters for this effect [7]: Pinf Psr

ð6Þ

where Rc is the coding rate of the assumed coding mode of the AMC, Eb is the energy cost to transmit one bit and L0 is the payload size of a transmitted packet (without the ECC overhead). For the calculation of Eq. (6), we assume: • •

• • •

ð5Þ

Moreover the expected energy cost due to transmission for a time interval/slot is C(t): L0 Eb b CðtÞ ¼ ð1 dÞ; Rc

not. In such a beaconed setting, ‘‘hello’’ messages are periodically exchanged among one-hop neighbors. The incurred additional energy consumption is independent of any other traffic. It is sensible to attempt to increase or even maximise the value of Pinf. In order to rationalize energy cost due to unnecessary (redundant) transmissions, we introduce— besides the successful reception probability Psr—alternative criteria that take into account the transmission costs. Such criteria (termed evaluation indicators) are three and denoted by fi, i = 1, 2, 3:

Constant cost per transmitted bit Eb The payload in the transmitted information is L0 and the transmitted message length becomes L0/Rc when convolutional ECC with rate Rc is used.

It is understood that the coding rate Rc, the forwarding probability b and the cure rate d are, in general, functions of time. For the energy cost calculations we assume that the computation costs are always one or two orders of magnitude lower than the transmission and reception costs [16] (similarly adopted in [6]) and may, hence, be safely ignored. In Eq. (6), the computation energy cost has been ignored on these grounds. 3.4 Rationale of the Proposed Algorithm Taking Eq. (5) into account, we can set as an objective of our algorithm the increase of the probability Psr. According to Eq. (4), this requires that the number of neighbors be known to each node. This assumption holds if each node overhears the beacons [5] from all neighbors, infected or

Successful reception probability (already mentioned), denoted by f1 = Psr Successful reception probability over cost, denoted by f2 = Psr/C Successful reception probability times energy cost gain, denoted by f3 = PsrH. The cost gain H is defined as the difference of the maximum cost Cmax minus the actual one. From Eq. (6) and [11], the maximum cost is incurred for Rc = and b = 1 and is equal to Cmax = 1.8 L0Eb. Hence the cost gain is

b H ¼ Psr L0 Eb 1:8 ð1 dÞ : Rc

ð7Þ

The evaluation indicator is always a function of the probability Psr and the energy cost, hence generically denoted as a function fi(Psr,C,j), when we refer to a policy j for indicator i. It is intuitive that the last two indicators mentioned try to achieve a compromise between energy cost and infection rate. To this end we propose the following algorithm: At every time iteration, every node measures the local SNR c(t) and this is compared to its value in the previous time slot c(t - 1). Then, the possible (candidate) new mode and b (l(t ? 1)cand and b(t ? 1)cand respectively) are calculated as follows: •

•

If c(t) \ c(t - 1) then the increase of the transmission probability by Db may be adopted as a method to counterbalance the trend of the infection probability to decrease due to noise. Moreover, the mode of the AMC can be decreased by one (step decrease) in order for a stronger ECC to be brought into effect. If c(t) [ c(t - 1) then the multiplicative decrease of the transmission probability by m * Db may be adopted as a method to suppress redundant transmissions. Moreover, the mode of the AMC can be (step) increased since a weaker ECC with lower overhead is considered adequate. mDb and m are termed the beta contraction (essentially the multiplicative decrease factor) and the contraction size, respectively. The above is described in Listing 1:

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Table 1 Candidate policies Policy

Policy description

Psr(t ? 1)

Cost C(t ? 1)

P1

Keep mode, change (increase or decrease) forwarding probability

ð1 dcand ðt þ 1ÞÞbcand ðt þ 1Þ ð1 PERn Þ

L0Ebb(t ? 1)cand/Rc

P2

Keep the same forwarding probability, change (increase or decrease) the AMC mode

ð1 dðt þ 1ÞÞbðt þ 1Þ 1 PERncand

L0Ebb(t)/Rc

P3

Change (increase or decrease) both.

P4

Keep both constant

ð1 dcand ðt þ 1ÞÞbcand ðt þ 1Þ 1 PERncand ð1 dcand ðt þ 1ÞÞbcand ðt þ 1Þ 1 PERncand

At every time slot and each node, four candidate policies are considered for the next slot to choose from. These policies are evaluated in terms of the value of the indicator utilized. Any of the aforementioned evaluation indicators may be used. To this end the corresponding Psr and C (cost) are calculated for all of them according to the equations of Table 1. The index cand denotes candidate values of tunable or affected parameters. The parameter PERcand is the estimated PER if the corresponding policy is chosen. It is calculated from Eq. (1) using the l(t ? 1)cand parameters (gn, an). In Table 2 we formalize the policy election. In this table, the symbols ?, - and * next to a parameter symbol stand for increment, decrement and no change of the parameter in question. In epidemic dissemination a considerable amount of energy is wasted on redundant transmissions in the attempt to spread the information within the network. In a setting similar to the one described here, energy is also wasted due to the use of unnecessarily strong error-control coding. We aim to tune the forwarding probability and the AMC mode in order to reduce the energy cost without compromising the infection rate. We assume that at time t, some nodes are infected and will broadcast the infecting information with probability

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cand

L0Ebb(t ? 1)cand/Rc

cand

L0Ebb(t)/Rc

Table 2 Policy election process l?

l-

l* P1

b?

Undefined

P3

b-

P3

Undefined

P1

b*

P2

P2

P4

(equal to the forwarding probability) bi(t) using the mode li(t) of the AMC, where i is the node index. The proposed scheme tries to increase successful reception probability with the right values of bi(t) and li(t), while keeping the energy cost low. In other words, the most suitable policy is sought, for maximizing the evaluation indicator in use. If this policy is indexed j then it holds that j 2 Pjfk ðjÞ ¼ maxðfk ðjÞÞ; j

ð8Þ

where j, k and P are the policy and indicator index and the set of policies respectively. The policy with the maximum evaluation indicator value is selected for the upcoming time slot. To resolve ties, a small random term h is added to the Psr values derived from the equations of Table 1 For all indicators fk their orders of magnitude should follow O(h) O(fk). The proposed algorithm is summarized in Listing 2.


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The symbols b(t)cand,j, l(t)cand,j stand for the candidate values of the tunable parameters corresponding to the policy j.

•

Full infection speed gain (or just speed gain), compared to the static scheme. This is defined as the normalized improvement of the T2FC attained by the scheme:

SG ¼ 4 Performance Evaluation and Discussion Depending on requirements and environment, in some sensor applications it is important to achieve extensive coverage of the network, without 100 % infection being critical. Sometimes speed of infection can be of considerable significance besides effective coverage. For this reason, some of the metrics we have selected for the performance evaluation of the proposed scheme are associated with the 90 % infection of the network: •

Time to full coverage (T2FC) and time to 90 % coverage (T29C): the time till all (or 90 % respectively) nodes are infected for the first time: T2FC ¼ minðsjIðsÞ ¼ 1Þ; T29C ¼ minðsjIðsÞ ¼ 0:9Þ ð9Þ

•

Energy to full coverage (E2FC) and to 90 % coverage (E29C): the energy cost spent till all (or 90 %) nodes are infected for the first time:

E2FC ¼ Cðt ¼ T2FCÞ;

•

E29C ¼ C ðt ¼ T29C Þ

ð10Þ

Energy cost gain compared to the static scheme case (AMC with a constant b). This metric is defined as follows:

GðtÞ ¼

C0 ðtÞ CðtÞ ; C0 ðtÞ

ð11Þ

where C0(t) and C(t) are the energy cost for a static scheme and the proposed scheme, respectively.

• •

T2FC0 T2FC T2FC0

ð12Þ

In Eq. (12), the index 0 denotes the T2FC for the static scheme. Gain product: the product of the last two gain metrics is defined as a new metric that quantifies the trade-off between energy cost reduction and speedy infection:

R ¼ SG GðT2FCÞ:

ð13Þ

Here the energy cost gain as defined in Eq. (11) can be calculated for the T2FC time interval. High non-negative values are pursued. Negative values of SG indicate a deceleration of infection. However, this may be an acceptable compromise when the energy cost gain is significant. We performed a number of simulations and monitored the established metrics. The simulation environment was set up with N = 50 nodes with random adjacencies, with a known probability of 0.85 that a node is neighbor to each one of the other nodes. Originally, a single node is infected. All the AMC pertinent details are adopted from [11] (including SNR range division, modulation types and parameter values). Moreover, the forwarding probability is initialized to a value (bi(1) = b0 = 0.4 Vi[{1, 2,…, N})., and afterwards controlled by our algorithm. In the constant b scheme, this value b0 is retained at all times by all nodes. We assume modest initial coding modes (li(1) = 3 Vi[{1, 2,…, N}). The beta step and beta contraction are set to Db = 0.02 and mDb = 7. 0.02 and 25 possible states are obtained for the forwarding probability. The cure probability is set at 0.1 of the forwarding probability at all times; this ensures that the epidemic shall not die out. Packet size (without encoding

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Int J Wireless Inf Networks (2014) 21:58–67 1 0.9

infected nodes ratio

0.8 0.7

evaluation indicator f1 evaluation indicator f2 evaluation indicator f3 AMC with constant forw. prob.

0.6 0.5 0.4 0.3 0.2 0.1 0 0

50

100

150

200

250

300

350

400

450

500

time, t

Fig. 1 Infected nodes ratio versus time. The proposed scheme is compared against a scheme with constant forwarding probability Normalized energy cost & infection speed for different indicators 1.6 AMC with constant beta eval. indicator f1

1.4

eval. indicator f

2

1.2

eval. indicator f

3

1 0.8 0.6 0.4 0.2 0

E2FC

E29C

T2FC

T29C

Fig. 2 Comparison between different schemes; all metrics normalized w.r.t. AMC with constant b, and hence, dimensionless

overhead) was assumed 50 bits. The AWGN gives a mean SNR = 3. Time is discrete and each iteration of the algorithm corresponds to a discrete time slot. The proposed scheme achieves considerable energy cost savings compared to the static scheme (AMC with constant b) case and successfully enhances the AMC in this direction. In Fig. 1, the infected nodes ratio for the proposed algorithm is shown in comparison to a static scheme. This plot helps visualize the fact that the speed of convergence is comparable to the case of stable b. The measurements presented in the bar chart of Fig. 2 allow a quantitative comparison between the considered models. The slowdown of the full infection is noticeable for some indicators, as already shown in Fig. 1, but the convergence speed is overall still comparable with existing

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schemes (AMC with constant b) with the additional benefit of extra energy cost reduction in the long term. The energy expenditure is significantly reduced even for both 90 % and full coverage. The behaviour is attributed to the combined regulation of the two parameters, namely the forwarding probability and the AMC coding mode, which is presented in Fig. 3. The measured values of the gain product for the different evaluation indicators are depicted in Fig. 4. Intuitively, the higher the (non-negative) product gain gets, the more efficient the scheme becomes. Negative values may be tolerated if they are due to a negative but small temporal gain. Naturally, we ignore the case that G(T2FC) \ 0 and SG \ 0 as this cancels the benefits of the proposed scheme. In Fig. 4 it is shown how the proposed scheme fares using the different evaluation indicators. The temporal behavior of the proposed scheme is affected by the beta step between allowed values of b (i.e. the Db) and by the beta contraction (mDb). Increasing the b in a noisy environment yields an increase in energy cost. The sharp contraction of the b (due to the AIMD) in a low-noise setting tends to counterbalance this impact. With the coding mode regulation, the overhead carried by each message is continuously modified. This implies a changing message length and, therefore, an accordingly changing energy cost. Another metric worth investigating is the popularity of each of the available policies. Which policy achieves the highest popularity depends on the evaluation indicator adopted. This is validated through Fig. 5 . To evaluate the effects of our approach, we have compared its performance with the original AMC in an epidemic setting, based on a fixed forwarding probability. We expand this evaluation with a comparison with the model in [9]. The approach in that work can be conceived as regulating the probability of reception through transmission range regulation in order to suppress the energy cost. In Fig. 6 the energy cost of the infection is depicted as well as the improvement offered by our approach. Energy cost is normalized over simulation duration and cost for one transmission and one reception (termed basic cost). Indicator f3 shows best performance in this aspect, almost doubling AMC the performance. Both works—ours and [9]—present a considerable improvement compared to their original non-adaptive versions. Their results are not directly comparable with each other due to the difference in time scales.

5 Conclusions and Future Work In this work we deal with the problem of information dissemination in ad hoc networks. Epidemic dissemination is utilized due to its reliability, efficiency and resilience to communication problems. We attempt to minimize the


a

65 3

b

0.45

2.8 evaluation indicator f1 evaluation indicator f2 evaluation indicator f3

2.6

AMC mode

forwarding probability

0.4

0.35

0.3

evaluation indicator f1 evaluation indicator f2 evaluation indicator f3 AMC with constant forw. prob.

2.4

2.2 0.25

0.2 0

2

50

100

150

200

250

300

350

400

450

500

1.8

0

100

200

300

400

500

time, t

time, t

Fig. 3 The temporal evolution of a the forwarding probability in our scheme and b average coding mode. Shown for all evaluation indicators. Time scale is in abstract time slots which correspond to algorithm iterations

Energy cost & full infection speed gain for different indicators 0.4

policy popularities for different indicators 0.35

0.3 0.3 0.2 0.25

0.1 0

0.2

−0.1 0.15

−0.2 −0.3

0.1

−0.4 G, energy cost gain SG, full inf. speed gain R, gain product

−0.5 −0.6

f_1

f_2

f_3

policy 1 policy 2 policy 3 policy 4

0.05

0

f_1

f_2

f_3

Fig. 4 Energy cost and full infection speed gain for the various indicators

Fig. 5 Popularities of policies P1, P2, P3 for different evaluation indicators f1, f2, f3. Presented as percentages

energy cost of this communication through channel noise awareness, thus introducing a cross-layer setup. To this end we proposed the expansion of the capabilities of an AMC scheme by combining it with an adaptive epidemic information dissemination algorithm. We propose that at each node the forwarding rate and the coding mode be tuned according to the assessment of a selected indicator. A proactive adaptation mechanism is proposed for the tuning of these parameters. This is in contrast to the reactive one suggested in [17]. The above can serve as a proof-of-concept serving data in diverse forms, from sensor readings to video and can find applications in deployments such as emergency response teams, security surveillance or habitat monitoring. The setting examined in this work matches

with the case of sensor-enabled nodes which disseminate the information they have previously received or generated. Performance assessment of the proposed scheme indicated that in the presence of AWGN, full infection is achieved at a modest energy cost by exploiting the tuned forwarding probability and coding mode. Achieving full coverage, while preserving energy, is a significant advantage in ad hoc networks and networks which operate in noisy environments. Given that the proposed scheme is one of the few context-aware adaptive epidemic models, in the future we aim to expand our investigation in this field. The optimization of adaptive epidemic dissemination schemes through minimization of the cost and/or maximization of suitably

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Energy cost per node, time unit and basic energy cost

66


400 AMC with const. forw. prob. eval. indicator f1

350

eval. indicator f

2

eval. indicator f3

300

250

200

150

100

0

0.2

0.4

0.6

0.8

1

Infection rate

Fig. 6 Infection rate against energy cost per node

defined utility functions is a challenging concept and requires a complex theoretical model. Our model could be additionally broadened with the study of environments with competition for radio resources. Further possible expansions of our work could include additional degrees of freedom, through modulation of e.g. the TTL value of packets [18]. Future work could also cover multi-epidemic settings, which include additional, partially infected states and cater for differentiation in the disseminated data [19].

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Author Biographies Theofanis Kontos received his B.Sc. in Physics and M.Sc. in Telecommunications from the University of Athens (UoA), Athens, Greece. He is currently pursuing a Ph.D. in pervasive computing at the same University. His research interests include Wireless Sensor Networks and ad hoc networks, epidemic information dissemination and topology discovery. He has also participated in numerous projects implemented in the context of the FP7 framework programme. He has extensive professional experience in wireless telecommunications and holds professional certifications in networking and security.

Int J Wireless Inf Networks (2014) 21:58–67 Christos Anagnostopoulos received his B.Sc., M.Sc. and Ph.D. in Informatics and Telecommunications from the Department of Informatics and Telecommunications (DIT) of the University of Athens (UoA), Athens, Greece. Since the beginning of 2011, he is a member of the faculty of the Ionian University, Department of Informatics, Corfu, Greece, where he serves as an Assistant Professor. Since August 2013, he has joined the School of Computing Science at the University of Glasgow, Glasgow, UK, as a post-doctoral fellow. His research interest is focused on mobile and distributed computing systems and context-aware computing. He has also participated in projects realized in the context of EU programs.

67 University of Aegean, Department of Information and Communication Systems Engineering. In 2002 he joined the faculty of the Hellenic Open University (Department of Informatics), Patras, Greece, as an assistant professor. Since the beginning of 2004, he belongs to the faculty of UoA, DIT where he presently is an assistant professor. He has participated in numerous projects realized in the context of EU programmes and national initiatives. His research interests are in the areas of mobile, pervasive computing, web systems engineering, and networked multimedia applications. He is the author of over 150 publications in these areas.

Stathes Hadjiefthymiades received his B.Sc., M.Sc. and Ph.D. in Informatics and Telecommunications from the Department of Informatics and Telecommunications (DIT) of the University of Athens (UoA), Athens, Greece. He also received a joint engineering– economics M.Sc. degree from the National Technical University of Athens. In 1992 he joined the Greek consulting firm Advanced Services Group, Ltd., as an analyst/developer of telematic applications and systems. In 1995 he became a member of the Communication Networks Laboratory of UoA. During the period 2001–2002, he served as a visiting assistant professor at the

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